WLTCL: Wide Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System

• URL: http://arxiv.org/abs/2504.18870v1
• Date: Sat, 26 Apr 2025 09:35:47 GMT
• Title: WLTCL: Wide Field-of-View 3-D LiDAR Truck Compartment Automatic Localization System
• Authors: Guodong Sun, Mingjing Li, Dingjie Liu, Mingxuan Liu, Bo Wu, Yang Zhang,
• Abstract summary: We propose an innovative wide field-of-view 3-D LiDAR vehicle compartment automatic localization system.<n>For vehicles of various sizes, this system leverages the LiDAR to generate high-density point clouds within an extensive field-of-view range.<n>Our compartment key point positioning algorithm utilizes the geometric features of the compartments to accurately locate the corner points.
• Score: 9.07574138083974
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As an essential component of logistics automation, the automated loading system is becoming a critical technology for enhancing operational efficiency and safety. Precise automatic positioning of the truck compartment, which serves as the loading area, is the primary step in automated loading. However, existing methods have difficulty adapting to truck compartments of various sizes, do not establish a unified coordinate system for LiDAR and mobile manipulators, and often exhibit reliability issues in cluttered environments. To address these limitations, our study focuses on achieving precise automatic positioning of key points in large, medium, and small fence-style truck compartments in cluttered scenarios. We propose an innovative wide field-of-view 3-D LiDAR vehicle compartment automatic localization system. For vehicles of various sizes, this system leverages the LiDAR to generate high-density point clouds within an extensive field-of-view range. By incorporating parking area constraints, our vehicle point cloud segmentation method more effectively segments vehicle point clouds within the scene. Our compartment key point positioning algorithm utilizes the geometric features of the compartments to accurately locate the corner points, providing stackable spatial regions. Extensive experiments on our collected data and public datasets demonstrate that this system offers reliable positioning accuracy and reduced computational resource consumption, leading to its application and promotion in relevant fields.

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