PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

2.2. Localization from point features 13
recovered. Typically a pose is estimated from 10 to 40 3D-2D correspondences. The visual
pose is then combined with measuresments from wheel odometry within a probabilistic SLAM
framework [78]. In fact, during frames with no detected visual landmarks navigation continues
based on wheel odometry. Map building is also vision based. The robot starts driving around in
a first unknown environment, building a world map. The 3D points in the map are associated
with SIFT features and an original view of the landmarks cropped from the original image.
Each landmark can have a set of associated SIFT features, describing the landmark for various
viewpoints. A 3D landmark is reconstructed from three images. Three images are taken in sequence
each in a distance of 20cm. Interest points are detected and matched between the three
images using SIFT feature matching. With a structure-from-motion approach the 3D landmarks
are reconstructed and the camera positions (robot positions) are computed. The landmarks are
reconstructed in a local coordinate frame. By adding this position to the current position of
the robot the landmarks are transformed into the global coordinate system and this position is
stored in the map database. Map building continues until all the environment has been traversed
and no new landmarks are found. The authors describe experiments for a 2-bedroom apartment.
Map building lasted 32 minutes and the robot created a map containing 82 landmarks. During
operation map updates are possible. Updates of the landmark position are maintained by a
Kalman filter [54]. The average localization error measured in the experiments is about 20cm to
25cm, which is quite high. However, it should be stressed that rather simple methods are used
in this approach to let the software run in real-time on low-cost computers. The approach of
Karlsson et al. is especially interesting as it is available as the commercial localization software
vSlam 1 for the robots sold by Evolution Robotics 2 . vSlam achieves map building and navigation
with a single low-cost camera. The most limiting factor of the approach, according to the
authors, is the size of the landmark database. Each landmark needs about 40kB to 500kB of
memory. This restricts the method to small indoor environments. Another critical issue which
should be discussed is the reconstruction of the landmarks during map building. A landmark
is reconstructed from three images at different positions. However, as the camera usually faces
forward the three views contain only translational forward motion. This imposes very bad conditions
for 3D reconstruction. In fact, reconstruction of a plane (e.g. wall) will show depth
estimation errors of about 10cm in practice. However, such uncertainties are handled within the
SLAM framework.
A different approach has been presented by Se, Lowe and Little [96]. In their work they
actually propose three different localization methods. The robot movement is however assumed
to be restricted to a plane, thus the pose estimate only contains 3 DOF. The map itself contains
full 3D coordinates of the landmarks. All three methods basically work by computing the pose
from 3D-3D landmark matches. The robot is equipped with a trinocular stereo head (Triclops 3 )
which produces 3D coordinates for each landmark in the current view. The first localization
approach is based on the Hough transform [48]. A 3D discretized Hough space representing
the robot poses with three parameters (X, Z, θ) is constructed. Each landmark match votes
for possible poses in the Hough space. The maximum vote then determines the parameters
(X, Z, θ) of the robot pose. The second proposed method is a RANSAC scheme [28]. From
two landmark matches the necessary translation and rotation for alignment and thus the robot
pose can be computed. This is repeated for a number of randomly chosen landmark samples
within a RANSAC scheme. For each sample the pose hypothesis is verified by checking how
1 http://www.evolution.com/core/navigation/vslam.masn
2 http://www.evolution.com
3 http://www.ptgrey.com