Cramer-Rao Lower Bound Analysis for Mobile Robot Navigation

摘要

This paper studies the Cramer-Rao Lower Bound (CRLB) of the simultaneous localization and map building (SLAM) problem for mobile robot navigation. Performance evaluation of SLAM is carried out and the Extended Kalman filtering (EKF) technique is verifed to be effective for the SLAM problem through the CRLB analysis. Detailed simulation and experimental results show that the process noise, measurement noise and feature number has influences on the CRLB of the SLAM.