Autonomous Localization and Motion Control of Under-Vehicle Inspection Robot

Abstract

This paper focuses on the train under-vehicle inspection robot equipped with 3D solid-state LiDAR and laser distance sensor. Dedicated 3D Simultaneous Localization and Mapping (SLAM) framework and motion control algorithm are proposed for the robot operating in the hazardous environment of the inspection pit. The constrained working environment includes repetitive and monotonous spatial features makes autonomous localization and navigation task a challenging issue. The proposed SLAM localization framework has a complete architecture that includes frontend laser odometry and loop closure detection. The method optimized for 3D solid-state LiDAR is utilized for feature extraction. The pose optimization problem is initialized with the motion estimation provided by wheel odometry. The wheel odometry interpolation is utilized to increase the pose update frequency. Moreover, the features of the vehicle wheels in the measurements of laser distance sensor are utilized for loop closure detection. The proposed motion control algorithm extracts the side features of the inspection pit for feedback of angular velocity closed-loop control. The experimental results from simulation and real-world show that the robot can be driven accurately along a straight trajectory in the inspection pit with a maximum integrated localization and navigation error of less than 0.015m in movement over 200m, which demonstrates the effectiveness of the proposed algorithms.