Abstract-Accurate and robust localization is essential for the successful navigation of autonomous mobile robots. The majority of existing localization approaches, however, is based on the assumption that the environment is static which does not hold for most practical application domains. In this paper, we present a localization framework that can robustly track a robot's pose even in non-static environments. Our approach keeps track of the observations caused by unexpected objects in the environment using temporary local maps. It relies both on these temporary local maps and on a reference map of the environment for estimating the pose of the robot. Experimental results demonstrate that by exploiting the observations caused by unexpected objects our approach outperforms standard localization methods for static environments.
Recent localization methods represent static, semi-static and dynamic objects in the map REF .
Temporary maps for robust localization in semi-static environments
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science