High-Precision Climbing Robot Localization Using Planar Array UWB/GPS/IMU/Barometer Integration

2025-09-28

AI Summary

To address the challenge of high-precision localization for climbing robots in high-altitude complex environments—characterized by severe GPS signal occlusion and large non-line-of-sight (NLOS) errors in Ultra-Wideband (UWB) ranging—this paper proposes a multi-source heterogeneous sensor fusion framework. We design a collaborative perception system integrating planar-array UWB, GPS, inertial measurement unit (IMU), and barometric altimeter, and develop an end-to-end neural network incorporating a multimodal attention mechanism to jointly infer UWB-based distances and barometric altitude. Furthermore, we tightly couple this neural estimator with an Unscented Kalman Filter (UKF) for robust state estimation. Experimental evaluation in real high-altitude scenarios demonstrates an average positioning error of 0.48 m and a maximum error of ≤1.50 m—significantly outperforming conventional baselines such as GPS/INS-EKF. The method exhibits superior robustness and environmental adaptability under challenging GNSS-denied and NLOS-prone conditions.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

To address the need for high-precision localization of climbing robots in complex high-altitude environments, this paper proposes a multi-sensor fusion system that overcomes the limitations of single-sensor approaches. Firstly, the localization scenarios and the problem model are analyzed. An integrated architecture of Attention Mechanism-based Fusion Algorithm (AMFA) incorporating planar array Ultra-Wideband (UWB), GPS, Inertial Measurement Unit (IMU), and barometer is designed to handle challenges such as GPS occlusion and UWB Non-Line-of-Sight (NLOS) problem. Then, End-to-end neural network inference models for UWB and barometer are developed, along with a multimodal attention mechanism for adaptive data fusion. An Unscented Kalman Filter (UKF) is applied to refine the trajectory, improving accuracy and robustness. Finally, real-world experiments show that the method achieves 0.48 m localization accuracy and lower MAX error of 1.50 m, outperforming baseline algorithms such as GPS/INS-EKF and demonstrating stronger robustness.

Problem

• Achieving high-precision localization for climbing robots
• Overcoming GPS occlusion and UWB NLOS challenges
• Integrating multiple sensors for robust navigation accuracy

Innovation

• Multi-sensor fusion with planar array UWB GPS IMU barometer
• Attention Mechanism Fusion Algorithm for adaptive data integration
• Unscented Kalman Filter refines trajectory for enhanced accuracy