Real-Time Geometric-Registration-Based Precision Localization for Autonomous Docking in Unstructured Factory Environment

Abstract

In factory distribution processes, autonomous mobile robots must dock precisely at base stations. However, this task is challenging due to the dynamic and unstructured nature of factory environments, as well as the sparse point clouds caused by sensor occlusions and distance limitations. To address these challenges, we propose a geometric registration approach designed to handle sparse point clouds in changing, unstructured settings. Our method utilizes the Hough transform to detect lines, describes the point cloud based on the relationships between these lines, filters out lines that do not correspond to the geometric features of the target base station, and estimates the pose of both the station and the robot using global registration techniques. We evaluated our system in four typical factory scenarios across 72 trials. Results show the robot achieved docking accuracy within $\pm$5.06 mm and $\pm 1.11^{\circ }$, with a 100% success rate in docking and correctly identifying the target cart from surrounding objects. This represents a 70% reduction in errors and an 86% increase in success rate compared to existing methods.