Improved Monte Carlo Localization with Robust Orientation Estimation for Mobile Robots

Abstract

This paper proposes an improved Monte Carlo Localization algorithm with robust orientation estimation (IMCLROE) by incorporating an orientation estimate and weight calculation mechanism to determine an optimal orientation for particles and a tournament selection to reduce the number of particles for position tracking. Based on previously established sensory information, the proposed IMCLROE can improve the computational efficiency. Localization accuracy and localization failure rate are also significantly improved during position tracking while maintaining a minimal population of particles. Experimental results have confirmed the effectiveness of the proposed approach.