Particle Filter and Finite Impulse Response Filter Fusion and Hector SLAM to Improve the Performance of Robot Positioning

Abstract

Indoor position estimation is essential for navigation; however, it is a challenging task mainly due to the indoor environments’ (a) high noise to signal ratio and (b) low sampling rate and (c) sudden changes to the environments. This paper uses a hybrid filter algorithm for the indoor positioning system for robot navigation integrating Particle Filter (PF) algorithm and Finite Impulse Response (FIR) filter algorithm to assure the continuity of the positioning solution. Additionally, the Hector Simultaneous Localisation and Mapping (Hector SLAM) algorithm is used to map the environment and improve the accuracy of the navigation. The paper implements the hybrid algorithm that uses the integrated PF, FIR, and Hector SLAM, using an embedded laser scanner sensor. The hybrid algorithm coupled with Hector SLAM is tested in several scenarios to evaluate the performance of the system, in terms of continuity and accuracy of the position estimation, and compares it with similar systems. The scenarios where the system is tested include reducing the laser sensor readings (low sampling rate), dynamic environments (change in the location of the obstacles), and the kidnapped robot situation. The results show that the system provides a significantly better accuracy and continuity of the position estimation in all scenarios, even in comparison with similar hybrid systems, except where there is a high and constant noise, where the performance of the hybrid filter and the simple PF seems almost the same.