Robust Navigation and Mapping Architecture for Large Environments

Abstract

Abstract This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for very large environments. A hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo localizer to resolve data association problems potentially present when returning to a known location after a large exploration period. Algorithms to improve the convergence of the Monte Carlo filter are presented that consider vehicle and sensor uncertainty. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions over robot pose in real time during the re‐localization process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed. © 2003 Wiley Periodicals, Inc.