Hybrid AMCL-EKF filtering for SLAM-based pose estimation in rough terrain

Abstract

The original approach of wheeled mobile robot localization in rough terrain, that connects local hybrid particles-Kalman filtering and global SLAM-based pose tracking, has been presented in this paper. Authors, basing on Adaptive Monte-Carlo Localization (AMCL) features of good resistance to unexpected errors, including slippages and kidnapping, use this particles filter together with the laser odometry and the inertial navigation for local pose estimation, which is performed by Extended Kalman Filter (EKF). In the proposed technique the MCL algorithm is used, as one of data sources for Kalman’s filter. Instead its regular performance of global localization. Global localization is based on Rao-Blackwellized SLAM technique with motion information estimated by EKF observations from Lidar sensor. The developed approach is based on Robot Operation System (ROS) framework and verified by V-REP simulations, in comparison to similar techniques. The reached results confirm the robustness and stability of the developed approach in inspection tasks of rough terrain.