Integrated Particle Filter Approach for Enhanced Indoor Robot Localization Using Multi-Sensor Fusion

Abstract

Mobile robots are increasingly used in rescue missions, household cleaning, and food service due to their stability and affordability. These robots require accurate positional data from sensors, environmental maps, and path-planning algorithms for effective navigation. The objective of this research paper is to present a novel approach to indoor robot positioning that integrates Particle Filter (PF) with Adaptive Particle Filter (APF) methodologies. The proposed system combines data from multiple sensors, including a Laser Range Finder (LRF), dual en-coders, and a gyroscopic unit, to enhance positional accuracy. By processing LRF measurements alongside reflected beacon signals, an algorithm is developed that dynamically adjusts particle distributions for improved localisation. Experimental results show that this integrated approach achieves an accuracy improvement of 96.5% as compared to traditional methods, demonstrating its potential for robust indoor navigation applications