Mobile Robot Localization via Indoor Positioning System and Odometry Fusion

2025-09-20

AI Summary

Indoor mobile robot localization suffers from multipath interference in standalone ultrasonic indoor positioning systems (IPS) and cumulative drift in wheel odometry. To address these limitations, this paper proposes a tightly coupled multi-sensor fusion method based on the extended Kalman filter (EKF), jointly estimating robot pose using IPS and wheel odometry. The approach leverages their complementary strengths: IPS provides global reference corrections, while odometry delivers high-frequency motion continuity; the EKF explicitly models and mitigates nonlinear uncertainties—including wheel slip and measurement noise. Experimental results demonstrate that the proposed fusion scheme reduces average localization error by approximately 62%, significantly suppresses trajectory drift, and markedly improves robustness and long-term stability compared to single-sensor baselines. This solution enables high-accuracy, infrastructure-light indoor autonomous navigation at low hardware cost.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Accurate localization is crucial for effectively operating mobile robots in indoor environments. This paper presents a comprehensive approach to mobile robot localization by integrating an ultrasound-based indoor positioning system (IPS) with wheel odometry data via sensor fusion techniques. The fusion methodology leverages the strengths of both IPS and wheel odometry, compensating for the individual limitations of each method. The Extended Kalman Filter (EKF) fusion method combines the data from the IPS sensors and the robot's wheel odometry, providing a robust and reliable localization solution. Extensive experiments in a controlled indoor environment reveal that the fusion-based localization system significantly enhances accuracy and precision compared to standalone systems. The results demonstrate significant improvements in trajectory tracking, with the EKF-based approach reducing errors associated with wheel slippage and sensor noise.

Problem

• Fusing indoor positioning with odometry for robot localization
• Overcoming wheel slippage and sensor noise limitations
• Enhancing trajectory tracking accuracy in indoor environments

Innovation

• Fuses ultrasound indoor positioning with wheel odometry
• Uses Extended Kalman Filter for sensor fusion
• Compensates wheel slippage and sensor noise limitations