Robust Simultaneous Localization and Mapping for Very Large Outdoor Environments

This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) when working in 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 type filter to resolve the data association problem potentially present when returning to a known location after a large exploration task. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions in real time. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed.