A Collaborative Navigation Model Based on Multi-Sensor Fusion of Beidou and Binocular Vision for Complex Environments

This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments.