A comparison of mobile robot pose estimation using nonlinear filters: simulation and experimental results

Abstract

This paper explores and compares the nature of the nonlinear filtering techniques on mobile robot pose estimation. Three nonlinear filters are implemented including the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF). The criteria of comparison is the magnitude of the error of pose estimation, the computational time, and the robustness of each filter to noise. The filters are applied to two applications including the pose estimation of a two-wheeled robot in an experimental platform and the pose estimation of a three-wheeled robot in a simulated environment. The robots both in the experimental and simulated platform move along a nonlinear trajectory like a circular arc or a spiral. The performance of their pose estimation are compared and analysed in this paper.