Mobile robot RBPF-SLAM with LMS sensor in indoor environments Application on RobuTER robot

Recently, Simultaneous Localization And Mapping (SLAM) problem becomes an active research field in mobile robotics. In addition, Rao-Blackwellized Particle Filter (RBPF) has been introduced as an effective means to solve this problem. This paper describes the implementation of a RBPF-SLAM to estimate the mobile robot pose while building the map of its surrounding indoor environment. In this approach, each particle in RBPF represents an individual map of the environment and a possible trajectory of the robot. This method is implemented and tested on the RobuTER mobile robot while exploiting the data delivered by the LMS sensor equipping it. The experimental results show the effectiveness of the proposed RBPF-SLAM in terms of (i) accuracy of the generated maps and (ii) the calculation of the actual robot pose.