An evaluation of the sequential Monte Carlo technique for simultaneous localisation and map-building

Abstract

Simultaneous localisation and map-building (SLAM) can be considered as a combined state and parameter estimation problem. Instead of using extended Kalman filtering, a more flexible Sequential Monte Carlo method is considered. Multiple generic particle filters are initialised to estimate the robot and obstacle positions concurrently. Simulation results based on a simple robot environment, which represents obstacles by line segments, indicate the feasibility of the proposed method.