  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
self-position. 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
self-positions out of large number of outlier hypotheses contaminated by
outlier measurements.
