Towards Constant-Time Robot Localization in Large Dynamic Environments

Global localization 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 global localization in static environments. However, online global localization in dynamic environments is still a difficult problem, due to incrementally arriving measurements as well as many outlier measurements. To realize a real time algorithm for such an online process, we have developed an incremental version of RANSAC algorithm by extending an efficient preemptive RANSAC scheme, in order to find inlier hypotheses of self-positions out of a large number of outlier hypotheses contaminated by the outlier measurements