Abstract:
The main contribution of this paper is a novel
stereo-based algorithm which serves as a tool to examine the
viability of stereo vision solutions to the simultaneous localisation
and mapping (SLAM) for large indoor environments. Using
features extracted from the scale invariant feature transform
(SIFT) and depth maps from a small vision system (SVS) stereo
head, an extended Kalman filter (EKF) based SLAM algorithm,
that allows the independent use of information relating to depth
and bearing, is developed. By means of a map pruning strategy
for managing the computational cost, it is demonstrated that
statistically consistent location estimates can be generated for
a small (6 m x 6 m) structured office environment, and in a
robotics search and rescue arena of similar size. It is shown
that in a larger office environment, the proposed algorithm
generates location estimates which are topologically correct, but
statistically inconsistent. A discussion on the possible reasons
for the inconsistency is presented. The paper highlights that,
despite recent advances, building accurate geometric maps of
large environments with vision only sensing is still a challenging
task.
