Pose-Based Visual Servoing with Lightweight Deep-Learning Binarization for Autonomous Mobile Robot Application

Pose-based visual servoing (PBVS) can complement the frequent drift issue of light detection and ranging (LiDAR) coordinate of LiDAR-based simultaneous localization and mapping (SLAM), navigation, and servoing technology, especially when autonomous mobile robot (AMR) works on the automatic docking alignment missions for automatic pallet engaging or automatic battery charging whose alignment precision requirement is extremely stricter. But PBVS occasionally suffers from the poor detected image quality of ARTag landmark to cause the reading drift or error. This paper proposes the lightweight deep-learning binarization method to preprocess the image quality of ARTag landmarks so that PBVS can evaluate the distance and pose between the ARTag landmark and the camera sensor more accurately, promptly, and steadily, for better feasibility of PBVS on the automatic docking alignment missions. Experimental results show, against conventional image-processing-based binarization, conventional computer-vision-based binarization, and conventional deep-learning-based binarization, the proposed lightweight deep-learning binarization not only raises the accuracy and reliability of ARTag’s reading, but also apparently improves the effectiveness and efficiency of PBVS’s operation, especially under environmental conditions of shadow occlusion, image blurring, low contrast, uneven illumination, or complex background.