Research on Multi Sensor Information Integration Inspection Data Collection of Four Legged Robot for Cable Tunnel

In response to the problems of low inspection accuracy and poor efficiency in traditional cable tunnel inspection methods, this paper proposes a research on multi-sensor information integration inspection data collection for cable tunnel quadruped robots. This paper integrates inspection data from three sensors: ultrasound, RGBD camera, and LiDAR. Based on deep learning to recognize inspection images, multi-sensor data is fused to achieve obstacle avoidance inspection of cable tunnel quadruped robots. The experimental results show that the proposed method can achieve synchronous and accurate collection of multi-sensor data, improve the accuracy of cable tunnel inspection, and ensure the safe and stable operation of the power system.