LiDAR-based perception system for logistics in industrial environments

Abstract

Autonomous vehicles in logistics and industrial environments demand robust and efficient perception systems. This study presents a LiDAR-based perception system designed for such environments, focusing on real-time deterministic obstacle detection and tracking with limited computational power. The proposed multi-stage approach leverages 3D data from LiDAR sensors. First, ground removal is performed to filter out static ground points. Then, a filtering step is applied using precomputed maps of the navigation area to filter out static zones from the LiDAR point clouds. After, object segmentation distinguishes structural elements from potential obstacles, followed by clustering and Principal Component Analysis (PCA) to accurately estimate obstacle pose and volume. An obstacle-tracking method ensures continuous monitoring over time. Extensive experiments in realistic logistics and industrial scenarios have been performed, comparing the proposed approach to state-of-the-art deep-learning-based methods, demonstrating the system’s high performance in both accuracy and efficiency.