An Improved Positioning Accuracy Method of a Robot Based on Particle Filter

TL;DR

This paper presents an enhanced robot positioning method using a particle filter to significantly improve accuracy in indoor navigation, especially under nonlinear and non-Gaussian conditions.

Contribution

It introduces a robust particle filter-based approach that reduces positioning errors in indoor robot navigation, demonstrating an 85.5% accuracy improvement.

Findings

Positioning accuracy improved by approximately 85.5%.

The method effectively handles nonlinear and non-Gaussian system challenges.

Experimental validation with a robot equipped with laser range finder, encoders, and gyro.

Abstract

This paper aims to improve the performance and positioning accuracy of a robot by using the particle filter method. The laser range information is a wireless navigation system mainly used to measure, position, and control autonomous robots. Its localization is more flexible to control than wired guidance systems. However, the navigation through the laser range finder occurs with a large positioning error while it moves or turns fast. For solving this problem, the paper proposes a method to improve the positioning accuracy of a robot in an indoor environment by using a particle filter with robust characteristics in a nonlinear or non-Gaussian system. In this experiment, a robot is equipped with a laser range finder, two encoders, and a gyro for navigation to verify the positioning accuracy and performance. The positioning accuracy and performance could improve by approximately 85.5% in this proposed method.