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A Multi-Sensor Fusion-Based Localization Method for a Magnetic Adhesion Wall-Climbing Robot.

Xiaowei Han¹, Hao Li², Nanmu Hui²

¹School of Mechanical Engineering, Shenyang University, Shenyang 110044, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study enhances robot localization on steel structures using fused sensor data. The novel method improves accuracy and robustness for complex infrastructure inspection.

Keywords:

Extended Kalman Filter (EKF)localization magnetic adhesion wall-climbing robot multi-sensor fusion residual weighting

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Area of Science:

Robotics
Sensor Fusion
Localization Algorithms

Background:

Magnetic adhesion robots face localization challenges on large steel structures due to visual occlusion, sensor drift, and environmental interference.
Accurate localization is critical for safe and efficient operation of wall-climbing robots in industrial settings.

Purpose of the Study:

To develop and evaluate a simulation-based multi-sensor fusion localization method for magnetic adhesion wall-climbing robots.
To improve the accuracy and robustness of robot localization on diverse steel structures.

Main Methods:

Integration of Inertial Measurement Unit (IMU), Wheel Odometry (Odom), and Ultra-Wideband (UWB) sensors.
Application of an Extended Kalman Filter (EKF) with a complementary filtering model for IMU and Odom data.
Implementation of a geometric residual-based weighting mechanism to optimize UWB ranging data.

Main Results:

The proposed method achieved a maximum localization error within 5 cm on both flat and spherical steel surfaces.
Demonstrated over 30% improvement in horizontal accuracy compared to baseline EKF approaches.
Exhibited consistent localization performance across varying surface geometries in simulated environments.

Conclusions:

The multi-sensor fusion approach significantly enhances localization accuracy and robustness for wall-climbing robots.
This method offers a viable solution for reliable robotic operations on large steel infrastructures.
Provides a foundation for future real-world deployments and further algorithm refinement.