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

2025-04-26

IEEE Transactions on Instrumentation and Measurement

AI Summary

To address challenges in cluttered logistics environments—including difficulty in automatic keypoint (e.g., corner) localization for multi-sized fence-type truck compartments, misalignment between LiDAR and robotic arm coordinate systems, and poor robustness—this paper proposes a wide-field-of-view 3D LiDAR-driven cargo compartment localization method. We innovatively design a parking-zone-constrained point cloud segmentation algorithm and establish a geometric-feature-driven structural modeling and corner refinement framework. Our approach achieves, for the first time, unified spatial coordinate alignment between the LiDAR and a mobile robotic arm. Evaluated on both proprietary and public datasets, the method attains centimeter-level localization accuracy, exhibits strong resilience to occlusion and clutter, and enables real-time inference. It has been successfully deployed in an intelligent loading/unloading system, enabling highly reliable autonomous loading and unloading operations.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: 3D Computer Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Precise automatic positioning of various-sized truck compartments
• Unified coordinate system for LiDAR and mobile manipulators
• Reliable positioning in cluttered environments

Innovation

• Wide field-of-view 3-D LiDAR for high-density point clouds
• Parking area constraints enhance point cloud segmentation
• Geometric features locate compartment corners accurately