An Efficient L-Shape Based Object Detection Using Density Distribution in LiDAR Point Cloud for Autonomous Driving

This paper presents a novel and efficient L-shape detection framework designed for autonomous driving applications. We propose a fast geometry-based L-shape fitting algorithm that ensures high computational efficiency and consistent execution across diverse data sources, while preserving accurate shape alignment. Furthermore, a cluster-wise, octree-based Bird's Eye View (BEV) classifier is introduced to determine both oriented and axis-aligned bounding box alignments. Extensive experiments validate the proposed method's effectiveness and accuracy in pose estimation, object detection, and speed estimation. The system achieves an object velocity estimation error within 0.5 meters per second. Real-world evaluations confirm the robustness and applicability of the approach in practical autonomous driving scenarios.