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ORB-SLAM: a versatile and accurate monocular SLAM system
Article in IEEE Transactions on Robotics · October 2015
DOI: 10.1109/TRO.2015.2463671
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Raul Mur-Artal
University of Zaragoza
J. M. M. Montiel
University of Zaragoza
Juan Domingo Tardos
University of Zaragoza
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ORB-SLAM: a Versatile and Accurate
Monocular SLAM System
Raul Mur-Artal*, J. M. M. Montiel, ´ Member, IEEE, and Juan D. Tardos, ´ Member, IEEE,
Abstract—This paper presents ORB-SLAM, a feature-based
monocular SLAM system that operates in real time, in small
and large, indoor and outdoor environments. The system is robust
to severe motion clutter, allows wide baseline loop closing and
relocalization, and includes full automatic initialization. Building
on excellent algorithms of recent years, we designed from scratch
a novel system that uses the same features for all SLAM tasks:
tracking, mapping, relocalization, and loop closing. A survival
of the fittest strategy that selects the points and keyframes of
the reconstruction leads to excellent robustness and generates a
compact and trackable map that only grows if the scene content
changes, allowing lifelong operation. We present an exhaustive
evaluation in 27 sequences from the most popular datasets. ORBSLAM achieves unprecedented performance with respect to other
state-of-the-art monocular SLAM approaches. For the benefit of
the community, we make the source code public.
Index Terms—Lifelong Mapping, Localization, Monocular Vision, Recognition, SLAM
BUNDLE ADJUSTMENT (BA) is known to provide ac- curate estimates of camera localizations as well as a
sparse geometrical reconstruction [
1], [2], given that a strong
network of matches and good initial guesses are provided. For
long time this approach was considered unaffordable for real
time applications such as Visual Simultaneous Localisation
and Mapping (Visual SLAM). Visual SLAM has the goal
of estimating the camera trajectory while reconstructing the
environment. Nowadays we know that to achieve accurate
results at non-prohibitive computational cost, a real time
SLAM algorithm has to provide BA with:
Corresponding observations of scene features (map
points) among a subset of selected frames (keyframes).
As complexity grows with the number of keyframes, their
selection should avoid unnecessary redundancy.
A strong network configuration of keyframes and points
to produce accurate results, that is, a well spread set of
keyframes observing points with significant parallax and
with plenty of loop closure matches.
An initial estimation of the keyframe poses and point
locations for the non-linear optimization.
A local map in exploration where optimization is focused
to achieve scalability.
The ability to perform fast global optimizations (e.g. pose
graph) to close loops in real-time.
This work was supported by the Direccion General de Investigaci ´ on of ´
Spain under Project DPI2012-32168, the Ministerio de Educacion Scholarship ´
FPU13/04175 and Gobierno de Aragon Scholarship B121/13. ´
The authors are with the Instituto de Investigacion en Ingenier ´ ´ıa de Aragon ´
(I3A), Universidad de Zaragoza, Mar´ıa de Luna 1, 50018 Zaragoza, Spain
(e-mail: [email protected]; [email protected]; [email protected]).
* Corresponding author.
The first real time application of BA was the visual odometry work of Mouragon et. al. [3], followed by the ground breaking SLAM work of Klein and Murray [4], known as Parallel
Tracking and Mapping (PTAM). This algorithm, while limited
to small scale operation, provides simple but effective methods
for keyframe selection, feature matching, point triangulation,
camera localization for every frame, and relocalization after
tracking failure. Unfortunately several factors severely limit
its application: lack of loop closing and adequate handling of
occlusions, low invariance to viewpoint of the relocalization
and the need of human intervention for map bootstrapping.
In this work we build on the main ideas of PTAM, the
place recognition work of Galvez-L ´ opez and Tard ´ os [ ´
5], the
scale-aware loop closing of Strasdat et. al [
6] and the use of
covisibility information for large scale operation [
7], [8], to
design from scratch ORB-SLAM, a novel monocular SLAM
system whose main contributions are:
Use of the same features for all tasks: tracking, mapping,
relocalization and loop closing. This makes our system
more efficient, simple and reliable. We use ORB features
9] which allow real-time performance without GPUs,
providing good invariance to changes in viewpoint and
Real time operation in large environments. Thanks to
the use of a covisibility graph, tracking and mapping is
focused in a local covisible area, independent of global
map size.
Real time loop closing based on the optimization of a
pose graph that we call the
Essential Graph. It is built
from a spanning tree maintained by the system, loop
closure links and strong edges from the covisibility graph.
Real time camera relocalization with significant invariance to viewpoint and illumination. This allows recovery
from tracking failure and also enhances map reuse.
A new automatic and robust initialization procedure based
on model selection that permits to create an initial map
of planar and non-planar scenes.
A survival of the fittest approach to map point and
keyframe selection that is generous in the spawning but
very restrictive in the culling. This policy improves tracking robustness, and enhances lifelong operation because
redundant keyframes are discarded.
We present an extensive evaluation in popular public
datasets from indoor and outdoor environments, including
hand-held, car and robot sequences. Notably, we achieve better
camera localization accuracy than the state of the art in direct
methods [
10], which optimize directly over pixel intensities
instead of feature reprojection errors. We include a discussion

in Section IX-B on the possible causes that can make featurebased methods more accurate than direct methods.
The loop closing and relocalization methods here presented
are based on our previous work [
11]. A preliminary version
of the system was presented in [
12]. In the current paper we
add the initialization method, the
Essential Graph, and perfect
all methods involved. We also describe in detail all building
blocks and perform an exhaustive experimental validation.
To the best of our knowledge, this is the most complete and
reliable solution to monocular SLAM, and for the benefit of
the community we make the source code public. Demonstration videos and the code can be found in our project webpage
A. Place Recognition
The survey by Williams et al. [13] compared several approaches for place recognition and concluded that techniques
based on appearance, that is image to image matching, scale
better in large environments than map to map or image to map
methods. Within appearance based methods, bags of words
techniques [
14], such as the probabilistic approach FAB-MAP
15], are to the fore because of their high efficiency. DBoW2
5] used for the first time bags of binary words obtained from
BRIEF descriptors [
16] along with the very efficient FAST
feature detector [
17]. This reduced in more than one order of
magnitude the time needed for feature extraction, compared to
18] and SIFT [19] features that were used in bags of
words approaches so far. Although the system demonstrated
to be very efficient and robust, the use of BRIEF, neither
rotation nor scale invariant, limited the system to in-plane
trajectories and loop detection from similar viewpoints. In
our previous work [
11], we proposed a bag of words place
recognizer built on DBoW2 with ORB [
9]. ORB are binary
features invariant to rotation and scale (in a certain range),
resulting in a very fast recognizer with good invariance to
viewpoint. We demonstrated the high recall and robustness
of the recognizer in four different datasets, requiring less than
39ms (including feature extraction) to retrieve a loop candidate
from a 10K image database. In this work we use an improved
version of that place recognizer, using covisibility information
and returning several hypotheses when querying the database
instead of just the best match.
B. Map Initialization
Monocular SLAM requires a procedure to create an initial
map because depth cannot be recovered from a single image.
One way to solve the problem is to initially track a known
structure [
20]. In the context of filtering approaches, points
can be initialized with high uncertainty in depth using an
inverse depth parametrization [
21], which hopefully will later
converge to their real positions. The recent semi-dense work
of Engel et al. [
10], follows a similar approach initializing the
depth of the pixels to a random value with high variance.
Initialization methods from two views either assumes locally
scene planarity [
4], [22] and recover the relative camera pose
from a homography using the method of Faugeras et. al [23],
or compute an essential matrix [
24], [25] that models planar
and general scenes, using the five-point algorithm of Nister
26], which requires to deal with multiple solutions. Both
reconstruction methods are not well constrained under low
parallax and suffer from a twofold ambiguity solution if all
points of a planar scene are closer to one of the camera centers
27]. On the other hand if a non-planar scene is seen with
parallax a unique fundamental matrix can be computed with
the eight-point algorithm [
2] and the relative camera pose can
be recovered without ambiguity.
We present in Section
IV a new automatic approach based
on model selection between a homography for planar scenes
and a fundamental matrix for non-planar scenes. A statistical
approach to model selection was proposed by Torr et al.
28]. Under a similar rationale we have developed a heuristic
initialization algorithm that takes into account the risk of
selecting a fundamental matrix in close to degenerate cases
(i.e. planar, nearly planar, and low parallax), favoring the
selection of the homography. In the planar case, for the sake of
safe operation, we refrain from initializing if the solution has
a twofold ambiguity, as a corrupted solution could be selected.
We delay the initialization until the method produces a unique
solution with significant parallax.
C. Monocular SLAM
Monocular SLAM was initially solved by filtering [20],
21], [29], [30]. In that approach every frame is processed
by the filter to jointly estimate the map feature locations and
the camera pose. It has the drawbacks of wasting computation
in processing consecutive frames with little new information
and the accumulation of linearization errors. On the other
hand keyframe-based approaches [
3], [4] estimate the map
using only selected frames (keyframes) allowing to perform
more costly but accurate bundle adjustment optimizations, as
mapping is not tied to frame-rate. Strasdat et. al [
31] demonstrated that keyframe-based techniques are more accurate than
filtering for the same computational cost.
The most representative keyframe-based SLAM system is
probably PTAM by Klein and Murray [
4]. It was the first work
to introduce the idea of splitting camera tracking and mapping
in parallel threads, and demonstrated to be successful for real
time augmented reality applications in small environments.
The original version was later improved with edge features, a
rotation estimation step during tracking and a better relocalization method [
32]. The map points of PTAM correspond to
FAST corners matched by patch correlation. This makes the
points only useful for tracking but not for place recognition. In
fact PTAM does not detect large loops, and the relocalization
is based on the correlation of low resolution thumbnails of the
keyframes, yielding a low invariance to viewpoint.
Strasdat et. al [
6] presented a large scale monocular SLAM
system with a front-end based on optical flow implemented
on a GPU, followed by FAST feature matching and
motiononly BA, and a back-end based on sliding-window BA. Loop
closures were solved with a pose graph optimization with
similarity constraints (7DoF), that was able to correct the scale

drift appearing in monocular SLAM. From this work we take
the idea of loop closing with 7DoF pose graph optimization
and apply it to the
Essential Graph defined in Section III-D
Strasdat et. al [7] used the front-end of PTAM, but performed the tracking only in a local map retrieved from a covisibility graph. They proposed a double window optimization
back-end that continuously performs BA in the inner window,
and pose graph in a limited-size outer window. However, loop
closing is only effective if the size of the outer window is
large enough to include the whole loop. In our system we
take advantage of the excellent ideas of using a local map
based on covisibility, and building the pose graph from the
covisibility graph, but apply them in a totally redesigned frontend and back-end. Another difference is that, instead of using
specific features for loop detection (SURF), we perform the
place recognition on the same tracked and mapped features,
obtaining robust frame-rate relocalization and loop detection.
Pirker et. al [
33] proposed CD-SLAM, a very complete
system including loop closing, relocalization, large scale operation and efforts to work on dynamic environments. However
map initialization is not mentioned. The lack of a public
implementation does not allow us to perform a comparison
of accuracy, robustness or large-scale capabilities.
The visual odometry of Song et al. [
34] uses ORB features
for tracking and a temporal sliding window BA back-end. In
comparison our system is more general as they do not have
global relocalization, loop closing and do not reuse the map.
They are also using the known distance from the camera to
the ground to limit monocular scale drift.
Lim et. al [
25], work published after we submitted our
preliminary version of this work [
12], use also the same
features for tracking, mapping and loop detection. However
the choice of BRIEF limits the system to in-plane trajectories.
Their system only tracks points from the last keyframe so the
map is not reused if revisited (similar to visual odometry)
and has the problem of growing unbounded. We compare
qualitatively our results with this approach in section
The recent work of Engel et. al [
10], known as LSDSLAM, is able to build large scale semi-dense maps, using
direct methods (i.e. optimization directly over image pixel
intensities) instead of bundle adjustment over features. Their
results are very impressive as the system is able to operate
in real time, without GPU acceleration, building a semi-dense
map, with more potential applications for robotics than the
sparse output generated by feature-based SLAM. Nevertheless
they still need features for loop detection and their camera
localization accuracy is significantly lower than in our system
and PTAM, as we show experimentally in Section
VIII-B. This
surprising result is discussed in Section
In a halfway between direct and feature-based methods is
the semi-direct visual odometry SVO of Forster et al. [
Without requiring to extract features in every frame they are
able to operate at high frame-rates obtaining impressive results
in quadracopters. However no loop detection is performed and
the current implementation is mainly thought for downward
looking cameras.
Finally we want to discuss about keyframe selection. All
visual SLAM works in the literature agree that running BA
Fig. 1. ORB-SLAM system overview, showing all the steps performed by
the tracking, local mapping and loop closing threads. The main components
of the place recognition module and the map are also shown.
with all the points and all the frames is not feasible. The
work of Strasdat et al. [
31] showed that the most costeffective approach is to keep as much points as possible,
while keeping only non-redundant keyframes. The PTAM
approach was to insert keyframes very cautiously to avoid
an excessive growth of the computational complexity. This
restrictive keyframe insertion policy makes the tracking fail in
hard exploration conditions. Our
survival of the fittest strategy
achieves unprecedented robustness in difficult scenarios by
inserting keyframes as quickly as possible, and removing later
the redundant ones, to avoid the extra cost.
A. Feature Choice
One of the main design ideas in our system is that the
same features used by the mapping and tracking are used
for place recognition to perform frame-rate relocalization and
loop detection. This makes our system efficient and avoids
the need to interpolate the depth of the recognition features
from near SLAM features as in previous works [
6], [7]. We
requiere features that need for extraction much less than 33ms
per image, which excludes the popular SIFT (
300ms) [19],
300ms) [18] or the recent A-KAZE (100ms) [35].
To obtain general place recognition capabilities, we require
rotation invariance, which excludes BRIEF [
16] and LDB [36].
We chose ORB [
9], which are oriented multi-scale FAST
corners with a 256 bits descriptor associated. They are extremely fast to compute and match, while they have good
invariance to viewpoint. This allows to match them from wide
baselines, boosting the accuracy of BA. We already shown the
good performance of ORB for place recognition in [
11]. While
our current implementation make use of ORB, the techniques
proposed are not restricted to these features.
B. Three Threads: Tracking, Local Mapping and Loop Closing
Our system, see an overview in Fig. 1, incorporates three
threads that run in parallel: tracking, local mapping and loop

closing. The tracking is in charge of localizing the camera
with every frame and deciding when to insert a new keyframe.
We perform first an initial feature matching with the previous
frame and optimize the pose using
motion-only BA. If the
tracking is lost (e.g. due to occlusions or abrupt movements),
the place recognition module is used to perform a global
relocalization. Once there is an initial estimation of the camera
pose and feature matchings, a local visible map is retrieved
using the covisibility graph of keyframes that is maintained
by the system, see Fig.
2(a) and Fig. 2(b). Then matches with
the local map points are searched by reprojection, and camera
pose is optimized again with all matches. Finally the tracking
thread decides if a new keyframe is inserted. All the tracking
steps are explained in detail in Section
V. The novel procedure
to create an initial map is presented in Section
The local mapping processes new keyframes and performs
local BA to achieve an optimal reconstruction in the surroundings of the camera pose. New correspondences for unmatched ORB in the new keyframe are searched in connected
keyframes in the covisibility graph to triangulate new points.
Some time after creation, based on the information gathered
during the tracking, an exigent point culling policy is applied
in order to retain only high quality points. The local mapping
is also in charge of culling redundant keyframes. We explain
in detail all local mapping steps in Section
The loop closing searches for loops with every new
keyframe. If a loop is detected, we compute a similarity transformation that informs about the drift accumulated in the loop.
Then both sides of the loop are aligned and duplicated points
are fused. Finally a pose graph optimization over similarity
constraints [
6] is performed to achieve global consistency. The
main novelty is that we perform the optimization over the
Essential Graph, a sparser subgraph of the covisibility graph
which is explained in Section
III-D. The loop detection and
correction steps are explained in detail in Section
We use the Levenberg-Marquardt algorithm implemented in
g2o [
37] to carry out all optimizations. In the Appendix we
describe the error terms, cost functions, and variables involved
in each optimization.
C. Map Points, KeyFrames and their Selection
Each map point pi stores:
Its 3D position Xw;i in the world coordinate system.
The viewing direction ni, which is the mean unit vector
of all its viewing directions (the rays that join the point
with the optical center of the keyframes that observe it).
A representative ORB descriptor Di, which is the associated ORB descriptor whose hamming distance is
minimum with respect to all other associated descriptors
in the keyframes in which the point is observed.
The maximum dmax and minimum dmin distances at
which the point can be observed, according to the scale
invariance limits of the ORB features.
Each keyframe
Ki stores:
The camera pose Tiw, which is a rigid body transformation that transforms points from the world to the camera
coordinate system.
(a) KeyFrames (blue), Current Camera (green), MapPoints (black, red),
Current Local MapPoints (red)
(b) Covisibility Graph
(c) Spanning Tree (green) and Loop
Closure (red)
(d) Essential Graph
Fig. 2. Reconstruction and graphs in the sequence
fr3 long office household
from the TUM RGB-D Benchmark [38].
The camera intrinsics, including focal length and principal point.
All the ORB features extracted in the frame, associated
or not to a map point, whose coordinates are undistorted
if a distortion model is provided.
Map points and keyframes are created with a generous policy, while a later very exigent culling mechanism is in charge
of detecting redundant keyframes and wrongly matched or not
trackable map points. This permits a flexible map expansion
during exploration, which boost tracking robustness under hard
conditions (e.g. rotations, fast movements), while its size is
bounded in continual revisits to the same environment, i.e.
lifelong operation. Additionally our maps contain very few
outliers compared with PTAM, at the expense of containing
less points. Culling procedures of map points and keyframes
are explained in Sections
VI-B and VI-E respectively.
D. Covisibility Graph and Essential Graph
Covisibility information between keyframes is very useful in
several tasks of our system, and is represented as an undirected
weighted graph as in [
7]. Each node is a keyframe and an edge
between two keyframes exists if they share observations of the
same map points (at least 15), being the weight
θ of the edge
the number of common map points.
In order to correct a loop we perform a pose graph optimization [
6] that distributes the loop closing error along the
graph. In order not to include all the edges provided by the
covisibility graph, which can be very dense, we propose to
build an
Essential Graph that retains all the nodes (keyframes),
but less edges, still preserving a strong network that yields

accurate results. The system builds incrementally a spanning
tree from the initial keyframe, which provides a connected
subgraph of the covisibility graph with minimal number of
edges. When a new keyframe is inserted, it is included in
the tree linked to the keyframe which shares most point
observations, and when a keyframe is erased by the culling
policy, the system updates the links affected by that keyframe.
Essential Graph contains the spanning tree, the subset
of edges from the covisibility graph with high covisibility
θmin = 100), and the loop closure edges, resulting in a strong
network of cameras. Fig.
2 shows an example of a covisibility
graph, spanning tree and associated essential graph. As shown
in the experiments of Section
VIII-E, when performing the
pose graph optimization, the solution is so accurate that an
additional full bundle adjustment optimization barely improves
the solution. The efficiency of the essential graph and the
influence of the
θmin is shown at the end of Section VIII-E.
E. Bags of Words Place Recognition
The system has embedded a bags of words place recognition
module, based on DBoW2
2 [5], to perform loop detection and
relocalization. Visual words are just a discretization of the
descriptor space, which is known as the visual vocabulary.
The vocabulary is created offline with the ORB descriptors
extracted from a large set of images. If the images are general
enough, the same vocabulary can be used for different environments getting a good performance, as shown in our previous
work [
11]. The system builds incrementally a database that
contains an invert index, which stores for each visual word
in the vocabulary, in which keyframes it has been seen, so
that querying the database can be done very efficiently. The
database is also updated when a keyframe is deleted by the
culling procedure.
Because there exists visual overlap between keyframes,
when querying the database there will not exist a unique
keyframe with a high score. The original DBoW2 took this
overlapping into account, adding up the score of images that
are close in time. This has the limitation of not including
keyframes viewing the same place but inserted at a different
time. Instead we group those keyframes that are connected
in the covisibility graph. In addition our database returns all
keyframe matches whose scores are higher than the
75% of
the best score.
An additional benefit of the bags of words representation
for feature matching was reported in [
5]. When we want
to compute the correspondences between two sets of ORB
features, we can constraint the brute force matching only to
those features that belong to the same node in the vocabulary
tree at a certain level (we select the second out of six),
speeding up the search. We use this
trick when searching
matches for triangulating new points, and at loop detection
and relocalization. We also refine the correspondences with an
orientation consistency test, see [
11] for details, that discards
outliers ensuring a coherent rotation for all correspondences.
The goal of the map initialization is to compute the relative
pose between two frames to triangulate an initial set of map
points. This method should be independent of the scene (planar
or general) and should not require human intervention to
select a good two-view configuration, i.e. a configuration with
significant parallax. We propose to compute in parallel two
geometrical models, a homography assuming a planar scene
and a fundamental matrix assuming a non-planar scene. We
then use a heuristic to select a model and try to recover the
relative pose with a specific method for the selected model.
Our method only initializes when it is certain that the twoview configuration is safe, detecting low-parallax cases and
the well-known twofold planar ambiguity [
27], avoiding to
initialize a corrupted map. The steps of our algorithm are:
1) Find initial correspondences:
Extract ORB features (only at the finest scale) in the
current frame
Fc and search for matches xc $ xr in the
reference frame
Fr. If not enough matches are found,
reset the reference frame.
2) Parallel computation of the two models:
Compute in parallel threads a homography
Hcr and a
fundamental matrix
xc = Hcr xr xT c Fcr xr = 0 (1)
with the normalized DLT and 8-point algorithms respectively as explained in [
2] inside a RANSAC scheme.
To make homogeneous the procedure for both models,
the number of iterations is prefixed and the same for
both models, along with the points to be used at each
iteration, 8 for the fundamental matrix, and 4 of them for
the homography. At each iteration we compute a score
SM for each model M (H for the homography, F for
the fundamental matrix):
SM = X
ρMd2 cr(xi c; xi r; M) + ρM(d2 rcxi c; xi r; M)
ρM(d2) = (Γ0 d2 if if d d2 2 < T TM M
cr and d2 rc are the symmetric transfer errors [2]
from one frame to the other.
TM is the outlier rejection
threshold based on the
χ2 test at 95% (TH = 5:99,
TF = 3:84, assuming a standard deviation of 1 pixel in
the measurement error).
Γ is defined equal to TH so that
both models score equally for the same
d in their inlier
region, again to make the process homogeneous.
We keep the homography and fundamental matrix with
highest score. If no model could be found (not enough
inliers), we restart the process again from step 1.
3) Model selection:
If the scene is planar, nearly planar or there is low
parallax, it can be explained by a homography. However
a fundamental matrix can also be found, but the problem
is not well constrained [
2] and any attempt to recover
the motion from the fundamental matrix would yield

wrong results. We should select the homography as the
reconstruction method will correctly initialize from a
plane or it will detect the low parallax case and refuse
the initialization. On the other hand a non-planar scene
with enough parallax can only be explained by the
fundamental matrix, but a homography can also be found
explaining a subset of the matches if they lie on a plane
or they have low parallax (they are far away). In this
case we should select the fundamental matrix. We have
found that a robust heuristic is to compute:


and select the homography if RH > 0:45, which
adequately captures the planar and low parallax cases.
Otherwise, we select the fundamental matrix.
4) Motion and Structure from Motion recovery:
Once a model is selected we retrieve the motion hypotheses associated. In the case of the homography
we retrieve 8 motion hypotheses using the method of
Faugeras et. al [
23]. The method proposes cheriality
tests to select the valid solution. However these tests
fail if there is low parallax as points easily go in front
or back of the cameras, which could yield the selection
of a wrong solution. We propose to directly triangulate
the eight solutions, and check if there is one solution
with most points seen with parallax, in front of both
cameras and with low reprojection error. If there is
not a clear winner solution, we do not initialize and
continue from step 1. This technique to disambiguate
the solutions makes our initialization robust under low
parallax and the twofold ambiguity configuration, and
could be considered the key of the robustness of our
In the case of the fundamental matrix, we convert it in
an essential matrix using the calibration matrix
Erc = KT Frc K (4)
and then retrieve 4 motion hypotheses with the singular
value decomposition method explained in [
2]. We triangulate the four solutions and select the reconstruction as
done for the homography.
5) Bundle adjustment:
Finally we perform a
full BA, see the Appendix for
details, to refine the initial reconstruction.
An example of a challenging initialization in the outdoor
NewCollege robot sequence [
39] is shown in Fig. 3. It can be
seen how PTAM and LSD-SLAM have initialized all points
in a plane, while our method has waited until there is enough
parallax, initializing correctly from the fundamental matrix.
V. T
In this section we describe the steps of the tracking thread
that are performed with every frame from the camera. The
camera pose optimizations, mentioned in several steps, consist
motion-only BA, which is described in the Appendix.
Fig. 3. Top: PTAM, middle LSD-SLAM, bottom: ORB-SLAM, some time
after initialization in the NewCollege sequence [
39]. PTAM and LSD-SLAM
initialize a corrupted planar solution while our method has automatically
initialized from the fundamental matrix when it has detected enough parallax.
Depending on which keyframes are manually selected, PTAM is also able to
initialize well.
A. ORB Extraction
We extract FAST corners at 8 scale levels with a scale factor
of 1.2. For image resolutions from
512 × 384 to 752 × 480
pixels we found suitable to extract 1000 corners, for higher
resolutions, as the
1241 × 376 in the KITTI dataset [40]
we extract 2000 corners. In order to ensure an homogeneous
distribution we divide each scale level in a grid, trying to
extract at least 5 corners per cell. Then we detect corners
in each cell, adapting the detector threshold if not enough
corners are found. The amount of corners retained per cell is
also adapted if some cells contains no corners (textureless or
low contrast). The orientation and ORB descriptor are then
computed on the retained FAST corners. The ORB descriptor
is used in all feature matching, in contrast to the search by
patch correlation in PTAM.
B. Initial Pose Estimation from Previous Frame
If tracking was successful for last frame, we use a constant
velocity motion model to predict the camera pose and perform
a guided search of the map points observed in the last frame. If
not enough matches were found (i.e. motion model is clearly
violated), we use a wider search of the map points around
their position in the last frame. The pose is then optimized
with the found correspondences.

C. Initial Pose Estimation via Global Relocalization
If the tracking is lost, we convert the frame into bag
of words and query the recognition database for keyframe
candidates for global relocalization. We compute correspondences with ORB associated to map points in each keyframe,
as explained in section
III-E. We then perform alternatively
RANSAC iterations for each keyframe and try to find a camera
pose using the PnP algorithm [
41]. If we find a camera
pose with enough inliers, we optimize the pose and perform
a guided search of more matches with the map points of
the candidate keyframe. Finally the camera pose is again
optimized, and if supported with enough inliers, tracking
procedure continues.
D. Track Local Map
Once we have an estimation of the camera pose and an
initial set of feature matches, we can project the map into the
frame and search more map point correspondences. To bound
the complexity in large maps, we only project a local map.
This local map contains the set of keyframes
K1, that share
map points with the current frame, and a set
K2 with neighbors
to the keyframes
K1 in the covisibility graph. The local map
also has a reference keyframe
Kref 2 K1 which shares most
map points with current frame. Now each map point seen in
K1 and K2 is searched in the current frame as follows:
1) Compute the map point projection
x in the current
frame. Discard if it lays out of the image bounds.
2) Compute the angle between the current viewing ray
and the map point mean viewing direction n. Discard if
v · n < cos(60).
3) Compute the distance
d from map point to camera
center. Discard if it is out of the scale invariance region
of the map point
d = 2 [dmin; dmax].
4) Compute the scale in the frame by the ratio
5) Compare the representative descriptor
D of the map
point with the still unmatched ORB features in the
frame, at the predicted scale, and near
x, and associate
the map point with the best match.
The camera pose is finally optimized with all the map points
found in the frame.
E. New Keyframe Decision
The last step is to decide if the current frame is spawned as
a new keyframe. As there is a mechanism in the local mapping
to cull redundant keyframes, we will try to insert keyframes as
fast as possible, because that makes the tracking more robust to
challenging camera movements, typically rotations. To insert
a new keyframe all the following conditions must be met:
1) More than 20 frames must have passed from the last
global relocalization.
2) Local mapping is idle, or more than 20 frames have
passed from last keyframe insertion.
3) Current frame tracks at least 50 points.
4) Current frame tracks less than 90% points than
Instead of using a distance criterion to other keyframes
as PTAM, we impose a minimum visual change (condition
4). Condition 1 ensures a good relocalization and condition
3 a good tracking. If a keyframe is inserted when the local
mapping is busy (second part of condition 2), a signal is sent
to stop local bundle adjustment, so that it can process as soon
as possible the new keyframe.
In this section we describe the steps performed by the local
mapping with every new keyframe
A. KeyFrame Insertion
At first we update the covisibility graph, adding a new node
Ki and updating the edges resulting from the shared map
points with other keyframes. We then update the spanning tree
Ki with the keyframe with most points in common.
We then compute the bags of words representation of the
keyframe, that will help in the data association for triangulating new points.
B. Recent Map Points Culling
Map points, in order to be retained in the map, must
pass a restrictive test during the first three keyframes after
creation, that ensures that they are trackable and not wrongly
triangulated, i.e due to spurious data association. A point must
fulfill these two conditions:
1) The tracking must find the point in more than the 25%
of the frames in which it is predicted to be visible.
2) If more than one keyframe has passed from map
point creation, it must be observed from at least three
Once a map point have passed this test, it can only be
removed if at any time it is observed from less than three
keyframes. This can happen when keyframes are culled and
when local bundle adjustment discards outlier observations.
This policy makes our map contain very few outliers.
C. New Map Point Creation
New map points are created by triangulating ORB from
connected keyframes
Kc in the covisibility graph. For each
unmatched ORB in
Ki we search a match with other unmatched point in other keyframe. This matching is done as
explained in Section
III-E and discard those matches that do
not fulfill the epipolar constraint. ORB pairs are triangulated,
and to accept the new points, positive depth in both cameras,
parallax, reprojection error and scale consistency are checked.
Initially a map point is observed from two keyframes but
it could be matched in others, so it is projected in the rest
of connected keyframes, and correspondences are searched as
detailed in section
D. Local Bundle Adjustment
The local BA optimizes the currently processed keyframe
Ki, all the keyframes connected to it in the covisibility graph
Kc, and all the map points seen by those keyframes. All other
keyframes that see those points but are not connected to the

currently processed keyframe are included in the optimization
but remain fixed. Observations that are marked as outliers are
discarded at the middle and at the end of the optimization.
See the Appendix for more details about this optimization.
E. Local Keyframe Culling
In order to maintain a compact reconstruction, the local
mapping tries to detect redundant keyframes and delete them.
This is beneficial as bundle adjustment complexity grows with
the number of keyframes, but also because it enables lifelong
operation in the same environment as the number of keyframes
will not grow unbounded, unless the visual content in the scene
changes. We discard all the keyframes in
Kc whose 90% of the
map points have been seen in at least other three keyframes in
the same or finer scale. The scale condition ensures that map
points maintain keyframes from which they are measured with
most accuracy. This policy was inspired by the one proposed
in the work of Tan et. al [
24], where keyframes were discarded
after a process of change detection.
The loop closing thread takes Ki, the last keyframe processed by the local mapping, and tries to detect and close
loops. The steps are next described.
A. Loop Candidates Detection
At first we compute the similarity between the bag of
words vector of
Ki and all its neighbors in the covisibility
graph (
θmin = 30) and retain the lowest score smin. Then we
query the recognition database and discard all those keyframes
whose score is lower than
smin. This is a similar operation
to gain robustness as the normalizing score in DBoW2,
which is computed from the previous image, but here we
use covisibility information. In addition all those keyframes
directly connected to
Ki are discarded from the results. To
accept a loop candidate we must detect consecutively three
loop candidates that are consistent (keyframes connected in
the covisibility graph). There can be several loop candidates
if there are several places with similar appearance to
B. Compute the Similarity Transformation
In monocular SLAM there are seven degrees of freedom
in which the map can drift, three translations, three rotations
and a scale factor [
6]. Therefore to close a loop we need to
compute a similarity transformation from the current keyframe
Ki to the loop keyframe Kl that informs us about the error
accumulated in the loop. The computation of this similarity
will serve also as geometrical validation of the loop.
We first compute correspondences between ORB associated
to map points in the current keyframe and the loop candidate
keyframes, following the procedure explained in section
At this point we have 3D to 3D correspondences for each
loop candidate. We alternatively perform RANSAC iterations
with each candidate, trying to find a similarity transformation
using the method of Horn [
42]. If we find a similarity Sil with
enough inliers, we optimize it (see the Appendix), and perform
a guided search of more correspondences. We optimize it again
and, if
Sil is supported by enough inliers, the loop with Kl is
C. Loop Fusion
The first step in the loop correction is to fuse duplicated
map points and insert new edges in the covisibility graph
that will attach the loop closure. At first the current keyframe
Tiw is corrected with the similarity transformation Sil
and this correction is propagated to all the neighbors of Ki,
concatenating transformations, so that both sides of the loop
get aligned. All map points seen by the loop keyframe and its
neighbors are projected into
Ki and its neighbors and matches
are searched in a narrow area around the projection, as done
in section
V-D. All those map points matched and those that
were inliers in the computation of
Sil are fused. All keyframes
involved in the fusion will update their edges in the covisibility
graph effectively creating edges that attach the loop closure.
D. Essential Graph Optimization
To effectively close the loop, we perform a pose graph
optimization over the
Essential Graph, described in Section
III-D, that distributes the loop closing error along the graph.
The optimization is performed over similarity transformations
to correct the scale drift [
6]. The error terms and cost function
are detailed in the Appendix. After the optimization each map
point is transformed according to the correction of one of the
keyframes that observes it.
We have performed an extensive experimental validation of
our system in the large robot sequence of NewCollege [
evaluating the general performance of the system, in 16 handheld indoor sequences of the TUM RGB-D benchmark [
evaluating the localization accuracy, relocalization and lifelong
capabilities, and in 10 car outdoor sequences from the KITTI
dataset [
40], evaluating real-time large scale operation, localization accuracy and efficiency of the pose graph optimization.
Our system runs in real time and processes the images
exactly at the frame rate they were acquired. We have carried
out all experiments with an Intel Core i7-4700MQ (4 cores
@ 2.40GHz) and 8Gb RAM. ORB-SLAM has three main
threads, that run in parallel with other tasks from ROS and
the operating system, which introduces some randomness in
the results. For this reason, in some experiments, we report
the median from several runs.
A. System Performance in the NewCollege Dataset
The NewCollege dataset [39] contains a 2.2km sequence
from a robot traversing a campus and adjacent parks. The
sequence is recorded by a stereo camera at 20 fps and a resolution
512×382. It contains several loops and fast rotations that
makes the sequence quite challenging for monocular vision.
To the best of our knowledge there is no other monocular
system in the literature able to process this whole sequence.
For example Strasdat et al. [
7], despite being able to close

Loop Detection (ms) Loop Correction (s)
Loop KeyFrames Essential Graph
Fusion Essential Graph Optimization Total (s)
1 287 1347 4.71 20.77 0.20 0.26 0.51
2 1082 5950 4.14 17.98 0.39 1.06 1.52
3 1279 7128 9.82 31.29 0.95 1.26 2.27
4 2648 12547 12.37 30.36 0.97 2.30 3.33
5 3150 16033 14.71 41.28 1.73 2.80 4.60
6 4496 21797 13.52 48.68 0.97 3.62 4.69


Thread Operation Median
TRACKING ORB extraction 11.10 11.42 1.61
Initial Pose Est. 3.38 3.45 0.99
Track Local Map 14.84 16.01 9.98
Total 30.57 31.60 10.39
KeyFrame Insertion 10.29 11.88 5.03
Map Point Culling 0.10 3.18 6.70
Map Point Creation 66.79 72.96 31.48
Local BA 296.08 360.41 171.11
KeyFrame Culling 8.07 15.79 18.98
Total 383.59 464.27 217.89

Fig. 4. Example of loop detected in the NewCollege sequence. We draw the
inlier correspondences supporting the similarity transformation found.
loops and work in large scale environments, only showed
monocular results for a small part of this sequence.
As an example of our loop closing procedure we show in
4 the detection of a loop with the inliers that support
the similarity transformation. Fig.
5 shows the reconstruction
before and after the loop closure. In red it is shown the local
map, which after the loop closure extends along both sides
of the loop closure. The whole map after processing the full
sequence at its real frame-rate is shown in Fig.
6. The big loop
on the right does not perfectly align because it was traversed
in opposite directions and the place recognizer was not able
to find loop closures.
We have extracted statistics of the times spent by each
thread in this experiment. Table
I shows the results for the
Fig. 5. Map before and after a loop closure in the NewCollege sequence.
The loop closure match is drawn in blue, the trajectory in green, and the local
map for the tracking at that moment in red. The local map is extended along
both sides of the loop after it is closed.
tracking and the local mapping. Tracking works at frame-rates
around 25-30Hz, being the most demanding task to track the
local map. If needed this time could be reduced limiting the
number of keyframes that are included in the local map. In
the local mapping thread the most demanding task is local
bundle adjustment. The
local BA time varies if the robot is
exploring or in a well mapped area, because during exploration
bundle adjustment is interrupted if tracking inserts a new
keyframe, as explained in section
V-E. In case of not needing
new keyframes local bundle adjustment performs a generous
number of prefixed iterations.
II shows the results for each of the 6 loop closures found. It can be seen how the loop detection increases
sublinearly with the number of keyframes. This is due to

Fig. 6. ORB-SLAM reconstruction of the full sequence of NewCollege. The
bigger loop on the right is traversed in opposite directions and not visual loop
closures were found, therefore they do not perfectly align.
the efficient querying of the database that only compare the
subset of images with words in common, which demonstrates
the potential of bag of words for place recognition. Our
Essential Graph includes edges around 5 times the number
of keyframes, which is a quite sparse graph.
B. Localization Accuracy in the TUM RGB-D Benchmark
The TUM RGB-D benchmark [38] is an excellent dataset
to evaluate the accuracy of camera localization as it provides
several sequences with accurate ground truth obtained with
an external motion capture system. We have discarded all
those sequences that we consider that are not suitable for pure
monocular SLAM systems, as they contain strong rotations,
no texture or no motion.
For comparison we have also executed the novel, direct,
semi-dense LSD-SLAM [
10] and PTAM [4] in the benchmark.
We compare also with the trajectories generated by RGBDSLAM [
43] which are provided for some of the sequences
in the benchmark website. In order to compare ORB-SLAM,
LSD-SLAM and PTAM with the ground truth, we align the
keyframe trajectories using a similarity transformation, as
scale is unknown, and measure the absolute trajectory error
(ATE) [
38]. In the case of RGBD-SLAM we align the trajectories with a rigid body transformation, but also a similarity to
check if the scale was well recovered. LSD-SLAM initializes
from random depth values and takes time to converge, therefore we have discarded the first 10 keyframes when comparing
with the ground truth. For PTAM we manually selected two
frames from which we get a good initialization. Table
shows the median results over 5 executions in each of the
16 sequences selected.
It can be seen that ORB-SLAM is able to process
all the sequences, except for
fr3 nostructure texture far
(fr3 nstr tex far). This is a planar scene that because of the
camera trajectory with respect to the plane has two possible
interpretations, i.e. the twofold ambiguity described in [
Our initialization method detects the ambiguity and for safety
refuses to initialize. PTAM initializes selecting sometimes
the true solution and others the corrupted one, in which
case the error is unacceptable. We have not noticed two
different reconstructions from LSD-SLAM but the error in this
sequence is very high. In the rest of the sequences, PTAM and
LSD-SLAM exhibit less robustness than our method, loosing
track in eight and three sequences respectively.
In terms of accuracy ORB-SLAM and PTAM are similar
in open trajectories, while ORB-SLAM achieves higher
accuracy when detecting large loops as in the sequence
fr3 nostructure texture near withloop (fr3 nstr tex near).
The most surprising results is that both PTAM and ORBSLAM are clearly more accurate than LSD-SLAM and
RGBD-SLAM. One of the possible causes can be that they
reduce the map optimization to a pose-graph optimization
were sensor measurements are discarded, while we perform
bundle adjustment and jointly optimize cameras and map over
sensor measurements, which is the gold standard algorithm to
solve structure from motion [
2]. We further discuss this result
in Section
IX-B. Another interesting result is that LSD-SLAM
seems to be less robust to dynamic objects than our system
as seen in
fr2 desk with person and fr3 walking xyz.
We have noticed that RGBD-SLAM has a bias in the scale
fr2 sequences, as aligning the trajectories with 7 DoF
significantly reduces the error. Finally it should be noted that
Engel et al. [
10] reported that PTAM has less accuracy than
fr2 xyz with an RMSE of 24.28cm. However,
the paper does not give enough details on how those results
were obtained, and we have been unable to reproduce them.
C. Relocalization in the TUM RGB-D Benchmark
We perform two relocalization experiments in the TUM
RGB-D benchmark. In the first experiment we build a map
with the first 30 seconds of the sequence
fr2 xyz and perform
global relocalization with every successive frame and evaluate
the accuracy of the recovered poses. We perform the same
experiment with PTAM for comparison. Fig.
7 shows the
keyframes used to create the initial map, the poses of the
relocalized frames and the ground truth for those frames. It
can be seen that PTAM is only able to relocalize frames
which are near to the keyframes due to the little invariance of
its relocalization method. Table
IV shows the recall and the
error with respect to the ground truth. ORB-SLAM accurately
relocalizes more than the double of frames than PTAM. In
the second experiment we create an initial map with sequence
fr3 sitting xyz and try to relocalize all frames from
fr3 walking xyz. This is a challenging experiment as there
are big occlusions due to people moving in the scene. Here
PTAM finds no relocalizations while our system relocalizes
78% of the frames, as can be seen in Table
IV. Fig. 8 shows
some examples of challenging relocalizations performed by
our system in these experiments.
D. Lifelong Experiment in the TUM RGB-D Benchmark
Previous relocalization experiments have shown that our
system is able to localize in a map from very different viewpoints and robustly under moderate dynamic changes. This
property in conjunction with our keyframe culling procedure

Absolute KeyFrame Trajectory RMSE (cm)

1.34 (1.34)
2.61 (1.42)
3.51 (3.51)
2.58 (2.52)
fr1 xyz
fr2 xyz
fr1 floor
fr1 desk
fr2 360
fr2 desk
fr3 long
393.3 (100.5)
9.50 (3.94)
X 38.53

fr3 nstr
tex far
4.92 /
34.74 18.31 –
fr3 nstr
tex near
1.39 2.74 7.54 –
fr3 str
tex far
0.77 0.93 7.95 –
fr3 str
tex near
1.04 X –
fr2 desk

fr3 sit
fr3 sit
fr3 walk
fr3 walk
0.63 X 31.73 6.97 (2.00)
0.79 0.83 7.73
1.34 X 5.87
1.24 X 12.44
1.74 X X

Results for ORB-SLAM, PTAM and LSD-SLAM are the median over 5
executions in each sequence. The trajectories have been aligned with 7DoF
with the ground truth. Trajectories for RGBD-SLAM are taken from the
benchmark website, only available for fr1 and fr2 sequences, and have been
aligned with 6DoF and 7DoF (results between brackets). X means that the
tracking is lost at some point and a significant portion of the sequence is not
processed by the system.
allows to operate lifelong in the same environment under
different viewpoints and some dynamic changes.
In the case of a completely static scenario our system is
able to maintain the number of keyframes bounded even if
the camera is looking at the scene from different viewpoints.
We demonstrate it in a custom sequence were the camera is
looking at the same desk during 93 seconds but performing
a trajectory so that the viewpoint is always changing. We
compare the evolution of the number of keyframes in our map
and those generated by PTAM in Fig.
9. It can be seen how
PTAM is always inserting keyframes, while our mechanism to
prune redundant keyframes makes its number to saturate.
While the lifelong operation in a static scenario should be a
requirement of any SLAM system, more interesting is the case
where dynamic changes occur. We analyze the behavior of our
system in such scenario by running consecutively the dynamic
sequences from
fr3: sitting xyz, sitting halfsphere, sitting rpy,
0.8 1 1.2 1.4 1.6
Top view
x [m]
y [m]
0.8 1 1.2 1.4 1.6
Frontal view
x [m]
z [m]
0.8 1 1.2 1.4 1.6
Top view
x [m]
y [m]
0.8 1 1.2 1.4 1.6
Frontal view
x [m]
z [m]
Fig. 7. Relocalization experiment in fr2 xyz. Map is initially created during
the first 30 seconds of the sequence (KFs). The goal is to relocalize subsequent
frames. Successful relocalizations (R) of our system and PTAM are shown.
The ground truth (GT) is only shown for the frames to relocalize.
Fig. 8. Example of challenging relocalizations (severe scale change, dynamic
objects) that our system successfully found in the relocalization experiments.
walking xyz, walking halfspehere and walking rpy. All the
sequences focus the camera to the same desk but perform
different trajectories, while people are moving and change
some objects like chairs. Fig.
10(a) shows the evolution of the
total number of keyframes in the map, and Fig.
10(b) shows for
each keyframe its frame of creation and destruction, showing
how long the keyframes have survived in the map. It can be
seen that during the first two sequences the map size grows as
all the views of the scene are being seen for the first time. In
10(b) we can see that several keyframes created during
these two first sequences are maintained in the map during
the whole experiment. During the sequences
sitting rpy and
walking xyz the map does not grow, because the map created
so far explains well the scene. In contrast, during the last two
sequences, more keyframes are inserted showing that there
are some novelties in the scene that were not yet represented,

Initial Map Relocalization
System KFs RMSE
Max. Error
fr2 xyz. 2769 frames to relocalize

fr3 walking xyz. 859 frames to relocalize



0 10 20 30 40 50 60 70 80 90 100
Time [s]
Fig. 9. Lifelong experiment in a static environment where the camera is
always looking at the same place from different viewpoints. PTAM is always
inserting keyframes, while ORB-SLAM is able to prune redundant keyframes
and maintains a bounded-size map.
due probably to dynamic changes. Finally Fig. 10(c) shows a
histogram of the keyframes according to the time they have
survived with respect to the remaining time of the sequence
from its moment of creation. It can be seen that most of the
keyframes are destroyed by the culling procedure soon after
creation, and only a small subset survive until the end of the
experiment. On one hand, this shows that our system has a
generous keyframe spawning policy, which is very useful when
performing abrupt motions in exploration. On the other hand
the system is eventually able to select a small representative
subset of those keyframes.
In these lifelong experiments we have shown that our map
grows with the content of the scene but not with the time,
and that is able to store the dynamic changes of the scene
which could be useful to perform some scene understanding
by accumulating experience in an environment.
E. Large Scale and Large Loop Closing in the KITTI Dataset
The odometry benchmark from the KITTI dataset [40]
contains 11 sequences from a car driven around a residential
area with accurate ground truth from GPS and a Velodyne
laser scanner. This is a very challenging dataset for monocular
vision due to fast rotations, areas with lot of foliage, which
make more difficult data association, and relatively high car
speed, being the sequences recorded at 10 fps. We play the
sequences at the real frame-rate they were recorded and ORBSLAM is able to process all the sequences by the exception
of sequence 01 which is a highway with few trackable close
objects. Sequences 00, 02, 05, 06, 07, 09 contain loops that
0 1000 2000 3000 4000 5000 6000
xyz halfsphere rpy xyz halfsphere rpy
sitting sitting sitting walking walking walking
(a) Evolution of the number of keyframes in the map
0 1000 2000 3000 4000 5000 6000
KeyFrame ID
xyz halfsphere rpy xyz halfsphere rpy
sitting sitting sitting walking walking walking
(b) Keyframe creation and destruction. Each horizontal line corresponds to
a keyframe, from its creation frame until its destruction
0 10 20 30 40 50 60 70 80 90 100
% Survival with respect to the remaining sequence time
% KeyFrames
(c) Histogram of the survival time of all spawned keyframes with respect to
the remaining time of the experiment
Fig. 10. Lifelong experiment in a dynamic environment from the TUM RGBD Benchmark.
were correctly detected and closed by our system. Sequence
09 contains a loop that can be detected only in a few frames
at the end of the sequence, and our system not always detects
it (the results provided are for the executions in which it was
Qualitative comparisons of our trajectories and the ground
truth are shown in Fig.
11 and Fig. 12. As in the TUM RGB-D
benchmark we have aligned the keyframe trajectories of our
system and the ground truth with a similarity transformation.
We can compare qualitatively our results from Fig.
11 and
12 with the results provided for sequences 00, 05, 06, 07
and 08 by the recent monocular SLAM approach of Lim et.
al [
25] in their figure 10. ORB-SLAM produces clearly more
accurate trajectories for all those sequences by the exception
of sequence 08 in which they seem to suffer less drift.
V shows the median RMSE error of the keyframe
trajectory over five executions in each sequence. We also
provide the dimensions of the maps to put in context the errors.
The results demonstrate that our system is very accurate being

-300 -200 -100 0 100 200 300
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
-300 -200 -100 0 100 200 300
x [m]
y [m]
Ground truth
ORB-SLAM + Global BA + 7DoF Alignment
-300 -200 -100 0 100 200 300
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
-300 -200 -100 0 100 200 300
x [m]
y [m]
Ground truth
ORB-SLAM + Global BA + 7DoF Alignment
-200 -150 -100 -50 0
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
-200 -150 -100 -50 0
x [m]
y [m]
Ground truth
ORB-SLAM + Global BA + 7DoF Alignment
Fig. 11. Sequences 00, 05 and 07 from the odometry benchmark of the KITTI dataset. Left: points and keyframe trajectory. Center: trajectory and ground
truth. Right: trajectory after 20 iterations of full BA. The output of our system is quite accurate, while it can be slightly improved with some iterations of BA.
-100 0 100 200 300 400 500 600
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(a) Sequence 02
-100 0 100 200 300 400 500
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(b) Sequence 03
-50 -40 -30 -20 -10 0 10 20 30 40 50
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(c) Sequence 04
-200 -150 -100 -50 0 50 100 150 200
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(d) Sequence 06
-400 -300 -200 -100 0 100 200 300 400 500
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(e) Sequence 08
-150 -100 -50 0 50 100 150 200 250 300 350
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(f) Sequence 09
0 100 200 300 400 500 600 700
x [m]
y [m]
Ground truth
ORB-SLAM + 7DoF Alignment
(g) Sequence 10
Fig. 12. ORB-SLAM keyframe trajectories in sequences 02, 03, 04 ,06, 08, 09 and 10 from the odometry benchmark of the KITTI dataset. Sequence 08
does not contains loops and drift (especially scale) is not corrected.


ORB-SLAM + Global BA (20 its.)
Sequence Dimension
KFs RMSE (m) RMSE (m) Time BA (s)
KITTI 00 564 × 496 1391 6.68 5.33 24.83
KITTI 01 1157 × 1827 X X X X
KITTI 02 599 × 946 1801 21.75 21.28 30.07
KITTI 03 471 × 199 250 1.59 1.51 4.88
KITTI 04 0:5 × 394 108 1.79 1.62 1.58
KITTI 05 479 × 426 820 8.23 4.85 15.20
KITTI 06 23 × 457 373 14.68 12.34 7.78
KITTI 07 191 × 209 351 3.36 2.26 6.28
KITTI 08 808 × 391 1473 46.58 46.68 25.60
KITTI 09 465 × 568 653 7.62 6.62 11.33
KITTI 10 671 × 177 411 8.68 8.80 7.64

the trajectory error typically around the 1% of its dimensions,
sometimes less as in sequence 03 with an error of the 0.3% or
higher as in sequence 08 with the 5%. In sequence 08 there
are no loops and drift cannot be corrected, which makes clear
the need of loop closures to achieve accurate reconstructions.
In this experiment we have also checked how much the
reconstruction can be improved by performing 20 iterations
full BA, see the Appendix for details, at the end of each
sequence. We have noticed that some iterations of
full BA
slightly improves the accuracy in the trajectories with loops but
it has negligible effect in open trajectories, which means that
the output of our system is already very accurate. In any case
if the most accurate results are needed our algorithm provides
a set of matches, which define a strong camera network, and
an initial guess, so that
full BA converge in few iterations.
Finally we wanted to show the efficacy of our loop closing
approach and the influence of the
θmin used to include edges
in the essential graph. We have selected the sequence 09 (a
very long sequence with a loop closure at the end), and in
the same execution we have evaluated different loop closing
strategies. In table
VI we show the keyframe trajectory RMSE
and the time spent in the optimization in different cases:
without loop closing, if we directly apply a
full BA (20 or
100 iterations), if we apply only pose graph optimization (10
iterations with different number of edges) and if we apply
pose graph optimization and
full BA afterwards. The results
clearly show that before loop closure, the solution is so far
from the optimal, that BA has convergence problems. Even
after 100 iterations still the error is very high. On the other
hand essential graph optimization shows fast convergence and
more accurate results. It can be seen that the choice of
θmin has
not significant effect in accuracy but decreasing the number
of edges the time can be significantly reduced. Performing
an additional BA after the pose graph optimization slightly
improves the accuracy while increasing substantially the time.

Method Time (s) Pose Graph Edges RMSE (m)
BA (20) 14.64 49.90
BA (100) 72.16 18.82
EG (200) 0.38 890 8.84
EG (100) 0.48 1979 8.36
EG (50) 0.59 3583 8.95
EG (15) 0.94 6663 8.88
EG (100) + BA (20) 13.40 1979 7.22

First row shows results without loop closing. Number between brackets for
BA (Bundle Adjustment) means number of Levenberg-Marquardt (LM)
iterations, while for EG (Essential Graph) is the
θmin to build the Essential
Graph. All EG optimizations perform 10 LM iterations.
100 0 100 200 300
x [m]
y [m]
Ground truth
(a) Without Loop Closing
100 0 100 200 300
x [m]
y [m]
Ground truth
(b) BA (20)
100 0 100 200 300
x [m]
y [m]
Ground truth
(c) EG (100)
100 0 100 200 300
x [m]
y [m]
Ground truth
(d) EG (100) + BA (20)
Fig. 13. Comparison of different loop closing strategies in KITTI 09.
A. Conclusions
In this work we have presented a new monocular SLAM
system with a detailed description of its building blocks and
an exhaustive evaluation in public datasets. Our system has
demonstrated that it can process sequences from indoor and
outdoor scenes and from car, robot and hand-held motions.
The accuracy of the system is typically below 1 cm in small
indoor scenarios and of a few meters in large outdoor scenarios
(once we have aligned the scale with the ground truth).
Currently PTAM by Klein and Murray [
4] is considered the
most accurate SLAM method from monocular video in real
time. It is not coincidence that the backend of PTAM is bundle
adjustment, which is well known to be the gold standard
method for the offline Structure From Motion problem [
One of the main successes of PTAM, and the earlier work of
Mouragnon [
3], was to bring that knowledge into the robotics
SLAM community and demonstrate its real time performance.

The main contribution of our work is to expand the versatility
of PTAM to environments that are intractable for that system.
To achieve this, we have designed from scratch a new monocular SLAM system with some new ideas and algorithms,
but also incorporating excellent works developed in the past
few years, such as the loop detection of Galvez-L ´ opez and ´
Tardos [ ´
5], the loop closing procedure and covisibility graph
of Strasdat [
6], [7], the optimization framework g2o
by Kuemmerle et. al [
37] and ORB features by Rubble et.
al [
9]. To the best of our knowledge, no other system has
demonstrated to work in as many different scenarios and with
such accuracy. Therefore our system is currently the most
reliable and complete solution for monocular SLAM. Our
novel policy to spawn and cull keyframes, permits to create
keyframes every few frames, which are eventually removed
when considered redundant. This flexible map expansion is
really useful in poorly conditioned exploration trajectories, i.e.
close to pure rotations or fast movements. When operating
repeatedly in the same environment, the map only grows if
the visual content of the scene changes, storing a history of
its different visual appearances. Interesting results for longterm mapping could be extracted analyzing this history.
Finally we have also demonstrated that ORB features have
enough recognition power to enable place recognition from
severe viewpoint change. Moreover they are so fast to extract
and match (without the need of multi-threading or GPU acceleration) that enable real time accurate tracking and mapping.
B. Sparse/Feature-based vs. Dense/Direct Methods
Recent real-time monocular SLAM algorithms such as
44] and LSD-SLAM [10] are able to perform dense
or semi dense reconstructions of the environment, while the
camera is localized by optimizing directly over image pixel
intensities. These direct approaches do not need feature extraction and thus avoid the corresponding artifacts. They are
also more robust to blur, low-texture environments and highfrequency texture like asphalt [
45]. Their denser reconstructions, as compared to the sparse point map of our system or
PTAM, could be more useful for other tasks than just camera
However, direct methods have their own limitations. Firstly,
these methods assume a surface reflectance model that in real
scenes produces its own artifacts. The photometric consistency
limits the baseline of the matches, typically narrower than
those that features allow. This has a great impact in reconstruction accuracy, which requires wide baseline observations
to reduce depth uncertainty. Direct methods, if not correctly
modeled, are quite affected by rolling-shutter, auto-gain and
auto-exposure artifacts (as in the TUM RGB-D Benchmark).
Finally, because direct methods are in general very computationally demanding, the map is just incrementally expanded
as in DTAM, or map optimization is reduced to a pose graph,
discarding all sensor measurements as in LSD-SLAM.
In contrast, feature-based methods are able to match features
with a wide baseline, thanks to their good invariance to
viewpoint and illumination changes. Bundle adjustment jointly
optimizes camera poses and points over sensor measurements.
In the context of structure and motion estimation, Torr and
Zisserman [
46] already pointed the benefits of feature-based
against direct methods. In this work we provide experimental
evidence (see Section
VIII-B) of the superior accuracy of
feature-based methods in real-time SLAM. We consider that
the future of monocular SLAM should incorporate the best of
both approaches.
C. Future Work
The accuracy of our system can still be improved incorporating points at infinity in the tracking. These points, which
are not seen with sufficient parallax and our system does not
include in the map, are very informative of the rotation of the
camera [
Another open way is to upgrade the sparse map of our
system to a denser and more useful reconstruction. Thanks
to our keyframe selection, keyframes comprise a compact
summary of the environment with a very high pose accuracy
and rich information of covisibility. Therefore the ORB-SLAM
sparse map can be an excellent initial guess and skeleton, on
top of which a dense and accurate map of the scene can be
built. A first effort in this line is presented in [
Bundle Adjustment (BA) [1]:
Map point 3D locations
Xw;j 2 R3 and keyframe poses
Tiw 2 SE(3), where w stands for the world reference, are
optimized minimizing the reprojection error with respect
to the matched keypoints
xi;j 2 R2. The error term for
the observation of a map point
j in a keyframe i is:

ei;j = xi;j πi(Tiw; Xw;j)
where πi is the projection function:

πi(Tiw; Xw;j) = ffi;u i;v xyz zi;j i;j i;j i;j ++ cci;v i;u#
xi;j yi;j zi;jT = RiwXw;j + tiw
Riw 2 SO(3) and tiw 2 R3 are respectively
the rotation and translation parts of
Tiw, and (fi;u; fi;v)
and (ci;u; ci;v) are the focal length and principle point
associated to camera
i. The cost function to be minimized

C = X
ρh(eT i;ji;j1ei;j)

where ρh is the Huber robust cost function and i;j =
i;jI2×2 is the covariance matrix associated to the scale
at which the keypoint was detected. In case of
full BA
(used in the map initialization explained in Section IV
and in the experiments in Section VIII-E) we optimize
all points and keyframes, by the exception of the first
keyframe which remain fixed as the origin. In
local BA
(see section VI-D) all points included in the local area
are optimized, while a subset of keyframes is fixed. In
pose optimization, or
motion-only BA, (see section V) all
points are fixed and only the camera pose is optimized.

Pose Graph Optimization over Sim(3) Constraints [6]:
Given a pose graph of binary edges (see Section
we define the error in an edge as:
ei;j = logSim(3)(Sij Sjw Siw1) (8)
Sij is the relative Sim(3) transformation between
both keyframes computed from the SE(3) poses just
before the pose graph optimization and setting the scale
factor to 1. In the case of the loop closure edge this
relative transformation is computed with the method of
Horn [
42]. The logSim3 [48] transforms to the tangent
space, so that the error is a vector in
R7. The goal is to
optimize the Sim(3) keyframe poses minimizing the cost
C = X
(eT i;jΛi;jei;j) (9)
Λi;j is the information matrix of the edge, which,
as in [
48], we set to the identity. We fix the loop closure
keyframe to fix the 7 degrees of gauge freedom. Although
this method is a rough approximation of a
full BA, we
demonstrate experimentally in Section
VIII-E that it has
significantly faster and better convergence than BA.
Relative Sim(3) Optimization:
Given a set of
n matches i ) j (keypoints and their
associated 3D map points) between keyframe
1 and
2, we want to optimize the relative Sim(3)
S12 (see Section VII-B) that minimizes
the reprojection error in both images:
e1 = x1;i π1(S12; X2;j)
e2 = x2;j π2(S121; X1;i) (10)
and the cost function to minimize is:
C = X
ρh(eT 1 1;i 1e1) + ρh(eT 2 2;j 1e2) (11)
1;i and 2;i are the covariance matrices associated to the scale in which keypoints in image 1 and image
2 were detected. In this optimization the points are fixed.
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Raul Mur Artal ´ was born in Zaragoza, Spain in
1989. He received the Industrial Engineering degree
(mention in Industrial Automation and Robotics) in
2012 and the M.S. degree in Systems and Computer Engineering in 2013 from the University of
Zaragoza, where he is currently working towards the
PhD. degree with the I3A Robotics, Perception and
Real-Time Group.
His research interests include Visual Localization
and Long-Term Mapping.
J. M . M. Montiel was born in Arnedo, Spain,
in 1967. He received the M.S. and Ph.D. degrees
in electrical engineering from the Universidad de
Zaragoza, Spain, in 1992 and 1996, respectively.
He is currently a Full Professor with the Departamento de Informatica e Ingenier ´ ´ıa de Sistemas,
Universidad de Zaragoza, where he is in charge
of Perception and Computer Vision research grants
and courses. His current interests include, real-time
vision localization and semantic mapping for rigid
and non rigid environments, and the transference of
this technology to robotic and nonrobotic application domains. Prof. Mart´ınez
Montiel is a member of the I3A Robotics, Perception, and Real-Time Group.
He has been awarded several Spanish MEC grants to fund research at the
University of Oxford, UK, and at Imperial College London, UK.
Juan D. Tardos ´ was born in Huesca, Spain, in 1961.
He earned the M.S. and Ph.D. degrees in electrical
engineering from the University of Zaragoza, Spain,
in 1985 and 1991, respectively. He is Full Professor
with the Departamento de Informatica e Ingenier ´ ´ıa
de Sistemas, University of Zaragoza, where he is in
charge of courses in robotics, computer vision, and
artificial intelligence. His research interests include
SLAM, perception and mobile robotics. Prof. Tardos ´
is a member of the I3A Robotics, Perception, and
Real-Time Group
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