LCSPose: Efficient, Accurate and Scalable Markerless 6-DoF Pose Estimation of a Quay Crane Spreader Based on LiDAR and Camera

Abstract

Accurate Six Degrees of Freedom (6-DoF) pose estimation of Ship-To-Shore (STS) quay crane spreaders is crucial for ensuring safe and efficient container handling in port automation. However, existing pose estimation techniques face significant challenges, as camera-based systems either rely on markers, which are prone to damage, or struggle with depth estimation inaccuracies. Additionally, 3D sensor-based approaches, particularly point cloud registration (PCR), face challenges such as initial pose errors, high-latency inference, and difficulties in object identification based purely on geometric features. To address these limitations, we propose LCSPose, a LiDAR-camera fusion-based 6-DoF pose estimation method that is marker-free, accurate, efficient, and scalable. Our approach integrates three key modules: (1) a semantic-geometric segmentation module for spreader segmentation and outlier removal, (2) a spatial consistency template sampling module based on Spatial Consistency Score (SC-Score) for reliable template selection across varying distances, and (3) a multi-view coarse-to-fine pose refinement module which incorporates multi-view PCA alignment for robust initial posture prior estimation and iterative pose refinement strategy for long-range registration. Our method demonstrates a 60% improvement in registration recall over state-of-the-art (SOTA) PCR methods, achieving up to 6 cm in translation error and 0.19 degrees in rotation error, while maintaining real-time processing at 20Hz.