Incremental RANSAC for Online Relocation in Large Dynamic Environments
Kanji Tanaka Eiji Kondo
Graduate School of Engineering
Kyushu University
Abstract:
Vehicle relocation is the problem in which a mobile
robot has to estimate the self-position with respect to an a
priori map of landmarks using the perception and the motion
measurements without using any knowledge of the initial selfposition.
Recently, RANdom SAmple Consensus (RANSAC), a
robust multi-hypothesis estimator, has been successfully applied
to offline relocation in static environments. On the other hand,
online relocation in dynamic environments is still a difficult
problem, for available computation time is always limited, and
for measurement include many outliers. To realize real time
algorithm for such an online process, we have developed an
incremental version of RANSAC algorithm by extending an
efficient preemption RANSAC scheme. This novel scheme named
incremental RANSAC is able to find inlier hypotheses of selfpositions
out of large number of outlier hypotheses contaminated
by outlier measurements.
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