Visual SLAM for Autonomous Ground Vehicles
Henning Lategahn, Andreas Geiger and Bernd Kitt

Abstract— Simultaneous Localization and Mapping (SLAM)
and Visual SLAM (V-SLAM) in particular have been an active
area of research lately. In V-SLAM the main focus is most often
laid on the localization part of the problem allowing for a drift
free motion estimate. To this end, a sparse set of landmarks
is tracked and their position is estimated. However, this set oflandmarks (rendering the map) is often too sparse for tasks inautonomous driving such as navigation, path planning, obstacle
avoidance etc. Some methods keep the raw measurements forpast robot poses to address the sparsity problem often resulting
in a pose only SLAM akin to laser scanner SLAM. For thestereo case, this is however impractical due to the high noiseof stereo reconstructed point clouds.
In this paper we propose a dense stereo V-SLAM algorithm that
estimates a dense 3D map representation which is more accurate
than raw stereo measurements. Thereto, we run a sparse VSLAM system, take the resulting pose estimates to compute a
locally dense representation from dense stereo correspondences.
This dense representation is expressed in local coordinate
systems which are tracked as part of the SLAM estimate. This
allows the dense part to be continuously updated. Our systemis driven by visual odometry priors to achieve high robustness
when tracking landmarks. Moreover, the sparse part of the
SLAM system uses recently published sub mapping techniques
to achieve constant runtime complexity most of the time. The
improved accuracy over raw stereo measurements is shown in a
Monte Carlo simulation. Finally, we demonstrate the feasibilityof our method by presenting outdoor experiments of a car likerobot.

I. I NTRODUCTION
A robot computing a map of a previously unknown environment while localizing itself within that map is referred
to as Simultaneous Localization and Mapping (SLAM) [31],[11], [5]. Recently, cameras have been used as the sole sensor
yielding visual SLAM (V-SLAM) [19], [7], [10].
The map is usually represented as a set of landmarks residing
in 3D space. The camera traverses its environment yielding
a trajectory. From each of the camera poses, a portion of thelandmarks is observed. From these measurements, the most
likely landmark positions and camera poses are estimated.
Common methods designed to solve the estimation problem
in real time rely on either Extended Kalman Filters (EKFs)[10], [25], [26], [9] or some variant of Bundle Adjustment(BA) [1], [29], [27], [30], [18]. However, both of theseclasses of methods are computationally very demanding.
Therefore one usually seeks to limit the number of landmarks
visible from each pose to remain computationally feasible. If
for instance one included every pixel of every camera frame
H. Lategahn and A. Geiger and B. Kitt are with the Department of Measurement and Control, Karlsruhe Institute of Technology, 76131 Karlsruhe
Germany. Email: henning.lategahn@kit.edu, geiger@kit.edu,
bernd.kitt@kit.edu

Fig. 1: The top shows a dense map computed by ouralgorithm. The bottom illustrates a map consisting of a sparseset of landmarks common in visual SLAM. The sparse map,
however, is often insufficient for autonomous vehicles.

as a single landmark one would need to estimate the positionof millions of landmarks after only a few time steps, thus
burying any hopes for real time operability.
For tasks common in autonomous driving such as path
planning, navigation, collision avoidance etc. these sparse
map representations are often insufficient. Instead a dense
map is sought. One way to address this problem is storing
raw sensor measurements for a given number of camera
poses. This is also common practice in laser scanner based
SLAM systems [6]. However, the raw measurements are very
noisy in the stereo camera case, making tasks in autonomous
driving quite difficult.
We herein present a stereo V-SLAM system which computes
dense maps that are more accurate than simply reconstructeddisparity images. Our system is composed of two main parts.
First a sparse V-SLAM system based on an EKF is run.
The state vector of the EKF contains all landmark positions,the current camera pose and a, yet to specify, subset ofpast camera poses. To tackle the computational complexity
problem inherent to EKF SLAM we utilize the sub mapping
method of [25], [26] dubbed conditionally independent sub
maps which has constant run time complexity most of thetime. After incorporating new observations and updating
the EKF state vector a new camera pose is obtained. We
assume the transition from the immediately foregoing poseto the current one to be of sufficiently high accuracy. This
assumption has also been made in [22] for a monocular setup.
During the second part, we compute a dense point cloud fromstereo. This dense point cloud is a local map. It is derived byfiltering disparity values for individual pixels of the image.
One Kalman Filter is used for every pixel taking the local ego
displacement for granted. The dense reconstruction of thesedisparities is expressed in local coordinate systems. Each oneof the past camera poses which are tracked during the sparse
V-SLAM spans one of these local coordinate systems. Put
differently, each past camera pose has a dense local map
attached. During loop closure the past camera poses are
updated and drift is resolved. Thereby the global positionof the local dense maps are also updated. The separationof the sparse map (part of the SLAM estimate) from thedense map (expressed in local coordinates, defined by the
SLAM state vector) is the main contribution of our work.
Figure 1 illustrates the difference between a sparse map
comprised of a few hundred landmarks (bottom) and a dense
map consisting of several magnitudes more points (top). The
dense reconstruction shows parked cars on the side of thestreet. Shadows of trees can be seen on the road surface.
The paper is organized as follows. In Section II we review
related work. In section III we present our method in moredetail. The sparse SLAM algorithm is introduced in section
III-A and the dense mapping is presented in section IIIB. Experimental results are given in section IV. Finally aconclusion is drawn and ideas for future research are given
in section V.
II. R ELATED W ORK
Agrawal and Konolige [1] compute a skeleton of poses.
Poses are connected by non-linear constraints obtained fromfeature matches between consecutive frames. The skeletongraph is then solved by BA. The map of their SLAM system
is obtained by reconstructing the stereo frames at the nodesof the pose graph. Their work differs from our method in
the underlying estimator (BA vs. EKF) and in the fact that
their dense part of the map is not locally smoothed.
The work closest to ours is probably the work of Nieto andco-workers [24], [23]. A hybrid metric mapping method thatembeds local coordinate systems to represent the dense map
in is presented. The local coordinate systems are spanned bythree or more landmarks of the state vector. The dense maps
consist of 2D occupancy grids amongst others. The denseparts are also locally estimated like in our method. However,their maps are only two dimensional. Moreover, care must
be taken to correctly choose appropriate landmarks to spaneach local coordinate system. Moreover, the scalability
problem of SLAM is not explicitly addressed.
The system developed by Franke and co-workers [12], [4]also aims at increasing accuracy of stereo reconstructions
by integrating disparity. However, their method relies ona known ego motion from external sensors. Moreover, themethod is only used locally and not embedded into a SLAM
framework.

III. A LGORITHM OVERVIEW
Figure 2 schematically shows the traversal of a robot. The
triangles denote robot poses, the stars denote a sparse setof landmarks. Solid triangles denote past poses which are
kept during estimation. The dense part is modeled by dense
point clouds in our algorithm. However, in Figure 2 it isdenoted by the “house” icons for clarity. Each past pose has
a portion of the dense map attached. The associated denseparts are entirely expressed in coordinate systems defined bypreceding poses.
Our algorithm consists of two phases which are both performed for every time step. During the first phase the setof landmarks {m1 , . . . , mM } ⊂ R3 , some of the past poses{Xt1 , Xt2 , . . . , XtN } and the current pose XT are stacked
into a vector which is sequentially estimated by an EKF
from stereo images. The poses are two spatial coordinatesand orientation Xt = (xt , yt , ψt )T . Implementation details
are given further below. After each EKF update one obtains
a new current pose XT . The transition from the immediately
foregoing pose to the current one (XT −1 → XT ) is used
to compute the dense representation of the environment
during the second phase. We integrate the disparity values
by individual Kalman Filters for every pixel. A thoroughdescription is given in section III-B. The thus computed 3D
position of the pixels are expressed with respect to one ofthe past poses of the EKF state vector. In Figure 2 the darkgray “houses” (corresponding to a portion of the dense map)are expressed in the coordinate system spanned by the pose
Xt1 whereas the white “houses” are in the coordinate system
of Xt2 etc. Therefore, each of the past poses has a small
portion of the dense map attached to it. After loop closing
a trajectory these past poses will be updated and hence willaffect the global position of the dense embeddings.
The two phases are detailed below in sections III-A and IIIB.
In the sequel we will assume that dense stereo correspondences are at our disposal. Dense disparity can nowadays
already be computed by FPGAs and efficient matchingalgorithms are emerging (e.g. [13]).
A. Sparse EKF SLAM
The details of basic EKF SLAM will not be reviewed here
for it being standard nowadays. For an introduction into thesubject the reader is referred to [31]. It is rather focused on
the prediction of the filter driven by visual odometry priors
and the sub mapping techniques which is used to reduce
computational complexity.
First a set of point-point matches between the previous andcurrent left image is computed. These matches are computedby a computationally lean algorithm. In our case we havematched sets of maxima in the sobel filtered images (after
non-maxima suppression). These putative matches are used
to compute a first guess of the ego displacement. To this end
the method of [16] is applied. The rotation matrix R andtranslation vector t is computed by non linear least squares.
However, first only the rotation R is estimated from points

Fig. 2: Triangles denote robot poses. Solid triangles are a subset of past poses which are kept as part of the sparse EKF
SLAM state. Stars denote landmarks. The dense map is schematically depicted by the house icons. The dense map ispartitioned into small fragments. Each such fragment is expressed in local coordinates defined by the associated pose. The
black houses are expressed with respect to Xt1 , the white houses with respect to Xt2 etc. XT is the current pose.

far away from the sensor by solving
X
2
R̂ = arg min||zi,t − proj(R · pi,t−1 )||
R

(1)

i

where zi,t is the measurement (pixel position and disparity)of the ith point match in the current frame and pi,t−1 isthe reconstructed matched point of the previous time step.
The stereo camera projection function is denoted by proj(·).
Thereafter the translation t is recovered while treating therotation R as known. Again, a non-linear least squares
problemX2(2)t̂ = arg minzi,t − proj(R̂ · pi,t−1 + t)todometry. New landmarks are initialized such that landmarks
are equally distributed over the entire image. Harris corners[14] have been used for landmark candidates.
To counteract the poor scalability of EKF SLAM algorithms
we use conditionally independent sub maps as proposed by
Piniés et al. in [25], [26]. The map is partitioned into local
sub maps each containing its own state vector. The size ofthe state vector of each sub map is bounded thus achieving
constant runtime complexity. At any time, a global update
can be performed by backpropagating current observations
down the sub map chain. More specifically two consecutive
sub maps A and B are modeled by two Gaussiansi

is solved for nearby point matches. Both steps are repeatedin a RANSAC scheme. The RANSAC scheme runs very
fast because only two matches are needed to generate ahypothesis for R and only one is required to compute atranslation hypothesis t which is why we have chosen thismethod. Computing visual odometry priors for SLAM has
been proposed in [2], [32].
This visual odometry is used to predict the position of thelandmarks in the current left image. Landmarks are tracked
by a KLT tracker [20]. If the KLT tracker yields a position
that deviates more than a threshold from its predicted position this point-to-landmark association is invalidated. The
filter state is also predicted from the visual odometry priors.
At first glance it may seem cumbersome to first compute
putative matches while thereafter actively searching for thelandmarks (KLT). We find that investing this extra time
is beneficial. Easily computable salient points yield unstable tracks whereas block matching search type methods
are computationally quite demanding. Well initialized KLTfeatures yield accurate and long lasting tracks. Also it iseasily possible to compute a much higher number of putative
matches than number of landmarks thus improving visualp (XA , XC |za )=p (XC , XB |za , zb )

=

 


PA,a PAC,a
N
PCA,a PC,a

 

X̂C,abPC,ab PCB,abN
PBC,ab PB,ab
X̂B,abX̂A,a
X̂C,awhere capital letters indicate map membership and lowercase
letters indicate the observations which have been used toestimate the states, that is za are all observations seen fromposes of sub map A and zb are measurements from sub map
B. C denotes a common part of the two sub maps. XC canbe interpreted as the overlap between the two maps. A backpropagation yields an estimate of the entire map modeled byp (XA , XC , XB |za , zb ) = X̂A,abPA,ab
N X̂C,ab  PCA,ab
PBA,ab
X̂B,ab(3)
PAC,ab
PC,ab
PBC,ab

PAB,ab
PCB,ab  PB,abwhere all observations (za , zb ) have been considered. Thisback propagating update step is accomplished by the follow-

ing equations

A. Simulation Results
K=−1
PAC,a PC,a

(4)

PAC,ab= KPC,ab

(5)

PA,ab

= PA,a + K(PCA,ab − PCA,a )(6)

X̂A,ab= X̂A,a + K(X̂C,ab − X̂C,a ).

(7)

After backpropagation, the global map (equation 3) is anexact solution to the non-submapped EKF SLAM. No
approximations are introduced by using the sub mapping
technique. Moreover, the backpropagation is linear in thenumber of sub maps and can be launched rarely yielding an
EKF SLAM algorithm which is constant most of the time.
For a detailed derivation of this method see [25], [26].
B. Dense Local Maps
It is a well known phenomena that stereo reconstructions
suffer from a high noise level in depth direction. Thisis caused by the uncertain disparity estimate from which
depth is inferred. For an investigation of this phenomenasee [28]. Therefore we filter disparities by iconic Kalmanfilters operating on each pixel of the image. The state of
the filter is the disparity. At this point we assume the ego
motion from pose XT −1 to XT to be known and sufficiently
exact. The filter states are predicted by reconstructing theprevious state (from pixel position and current disparity
state), compensating the ego motion and backprojecting it
into the current image. More specifically, let u, v be thepixel position with Kalman filter state dT −1 . The prediction
is computed by
pT −1

=

reconst(u, v, dT −1 )

(8)

p̄T =


¯
ūT , v̄T , dT=

Φ (pT −1 |XT −1 , XT )(9)proj(p̄T )(10)where ¯· denotes prediction, reconst(·) is the stereo reconstruction and Φ(·) compensates ego motion. The predicted
pixel position ūT , v̄T is used for association. Once a track islost it is added to the local coordinate system permanently.
If a loop closure occurs, the displacement from XT −1 to
XT may be too large. In such cases the ego motion iscompensated by the visual odometry priors (equations 1, 2)
i.e. p̄T = Φ(pT −1 |R̂, t̂).
This method of increasing depth accuracy for stereo cameras
has been used in [15], [12], [4]. All these methods are basedon the work of Matthies and co-workers [21].
Observe, that the dense part of the map is tracked incamera coordinates. However, for our method, it needs tobe transformed into the coordinate system which is spannedby the last pose XtN = (xtN , ytN , ψtN )T that has beenpermanently added to the state vector.
IV. E XPERIMENTAL R ESULTS
In the following we present experimental results. First wevalidate our claim, that the iconic Kalman Filters improvereconstruction accuracy. We show that the standard deviationof the depth of a point is considerably lower compared to
the non-filtered points. Second, we show results of a 3D map
computed from a real world sequence.

To assess the improvement in depth accuracy we have run
a Monte Carlo simulation as follows. First we generate a
trajectory of the camera. Then landmarks are placed aroundthe trajectory. During the simulation run, the camera traverses
the environment and perceives the landmarks once they
are within a predefined distance to the camera. The exact
pixel positions and disparities, which are computed from thelocations of the landmarks, current camera pose and camera
parameters is disturbed by Gaussian noise with covariance2
I. This is taken as the measurement and fed
matrix σcam
into the SLAM algorithm. The exact ego motion (yaw rateand speed) is also disturbed by Gaussian noise with standard
deviations σyaw , σspeed . The disturbed ego motion is anapproximation of the ego motion priors of equations 1 and2. This part of the simulation only affects the sparse SLAM
system.
The dense local map is simulated by 1000 independent tracksof the same point. This point is also added to the simulation
environment. The virtual camera perceives this point (pixel
position and disparity) 1000-fold for each time step, again
disturbed by independent Gaussian noise with covariance2
σcam
I. Each occurrence of this point is tracked by an iconic
Kalman Filter as presented before. At each time step, all of
the tracks of the dense local map are reconstructed and theempirical standard deviation of the depth is computed. By
using many realizations of the same point (1000 in our case)
we obtain a faithful approximation of the expected accuracy.
Note that this point is not used as a landmark in the sparse
SLAM system.
The base width of the stereo camera setup used in our
simulation is 57.5 cm. The standard deviation of the disparity
matcher is set to σcam = 1.0px and the ego motion ismdisturbed by σyaw = 0.3 rads and σspeed = 0.6 s . The egomvelocity is 10 s and points are observed at 10 Hz. Figure
3 shows the mean depth of the iconically tracked point
over time in green. It is first observed by the camera at a
distance of 40 meters. The empirical standard deviation of
the depth of all tracks is shown as a confidence interval inblue (amplified by a factor of five for better visibility).
Moreover, we have reconstructed each occurrence of thepoint of the dense local map from its non-filtered measurements. The empirical standard deviation of the depth iscomputed for every time step and plotted in red in Figure 3(again five-fold amplified). It can be seen that the accuracyof the filtered point is considerably better.
B. Real World Experiments
In the following we present experimental results of a car
like robot.
We have equipped our testing vehicle with stereo cameras
with a base width of approximately 57.5 cm. The regionof interest of our cameras is of size 1350×370 pixels with
an opening angle of 90 degrees. The disparity images arecomputed by a block matching based stereo matcher as
presented in [13]. The stereo matcher produces very dense
disparity images which plays into our hands nicely. Images

Fig. 5: The bottom part of the dense map before (left) and immediately after (right) closing the loop. A slight miss alignment
can be observed on the left bottom of the loop. After loop closure this miss alignment is successfully resolved. The positionof past poses is shown in Figure 4.

are recorded with 10 Hz. We have driven a loopy trajectoryof approximately 520 meters on our campus with speeds of
up to 30 km
h . Note that the loop closure has been triggered
manually since place recognition is not the focus of ourcontribution.
Figure 4 depicts the chain of past poses Xt1 , . . . , XtN froma bird’s eye perspective. The left part of Figure 4 shows theposes directly before closing the loop. On the bottom left
corner a drift of approximately 5 m can be observed. The
right part of the Figure shows the past poses immediately
after closing the loop. However, these poses are only part ofthe sparse mapping part of our algorithm.
Each one such pose has a portion of the dense map attached
to it. The resulting dense maps are shown in Figure 5. Again
the left half illustrates the dense map immediately before
closing the loop whereas the right half shows the dense map
after loop closure. It can be seen how the left bottom cornerof the map is misaligned directly before the drift is resolved.
After loop closure, however, the dense map fragments alignwell.
Our system is mostly implemented in MATLAB and runningtimes are currently not real-time. However, we plan torefactor our system and reimplement a C++ version whichis expected to reach real-time capabilities.

Fig. 3: Results of a Monte Carlo simulation are illustrated.
The mean depth of 1000 independent Kalman tracks of thesame point of the dense local map is shown over timein green. The empirical standard deviation is depicted inblue (five-fold amplified for clarity). For comparison, theempirical standard deviation of the raw reconstruction (nonfiltered) is shown in red. A considerable improvement of theaccuracy of the filtered point over the raw reconstruction canbe seen.

V. C ONCLUSION AND F UTURE R ESEARCH
Herein we have presented a method to compute dense local
maps which are embedded into a sparse SLAM algorithm.
The dense local maps are continuously updated and 3D
global maps are obtained after loop closure. Moreover, it was
shown experimentally that our dense maps are potentiallymore accurate than raw stereo reconstructions.
The sparse SLAM algorithm estimates a subset of past robot
poses jointly with a sparse set of landmarks. This estimationis achieved by conditionally independent sub maps [25],[26] which are based on an EKF and has constant runtime
complexity most of the time. The prediction of the filter stateis driven by visual odometry priors computed from a set ofcheaply computed point matches.
The dense map is computed from the local ego displacement(from pose to pose) and dense disparity images. To increase

reconstruction accuracy, the disparities are tracked by iconic
Kalman filters. Their reconstructed 3D position is expressedin coordinate systems spanned by past robot poses which arepart of the sparse EKF SLAM state vector.
The presented algorithm brings some flexibility. The sparse
SLAM algorithm can easily be replaced by any one of the
published SLAM algorithms. It can thereby be tuned to
specific needs. Moreover, the dense local mapping algorithm
which merely needs the ego displacement and dense disparity
images can be enhanced as long as the map is expressed inlocal coordinates.
In the future we plan to integrate an appearance based place
recognition engine into our SLAM system to automaticallydetect loop closures. The method of Cummnis and Newman

Fig. 4: The set of past robot poses which span a local
coordinate system each is depicted. On the left the position
directly before loop closure is shown. The right shows the
position immediately after loop closure. The driving direction
is counter clockwise. The bottom part of the dense map forthat trajectory is depicted in Figure 5.

[8] seems promising. Moreover, we believe that developing
more advanced locally dense mapping algorithms is exciting
ground for future research. Specifically, we plan to fusedense optical flow and disparity. Dense flow algorithms
are becoming increasingly feasible [17]. Furthermore, thedetection and tracking of moving objects (e.g. [3]) as anintermediate step will improve the operation in dense urban
traffic.
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