Laboratoire d'Informatique (LIG), University of Grenoble Alpes, 38000 Grenoble, France.
Sensors (Basel). 2023 May 12;23(10):4720. doi: 10.3390/s23104720.
Light Detection and Ranging (LiDAR) technology is now becoming the main tool in many applications such as autonomous driving and human-robot collaboration. Point-cloud-based 3D object detection is becoming popular and widely accepted in the industry and everyday life due to its effectiveness for cameras in challenging environments. In this paper, we present a modular approach to detect, track and classify persons using a 3D LiDAR sensor. It combines multiple principles: a robust implementation for object segmentation, a classifier with local geometric descriptors, and a tracking solution. Moreover, we achieve a real-time solution in a low-performance machine by reducing the number of points to be processed by obtaining and predicting regions of interest via movement detection and motion prediction without any previous knowledge of the environment. Furthermore, our prototype is able to successfully detect and track persons consistently even in challenging cases due to limitations on the sensor field of view or extreme pose changes such as crouching, jumping, and stretching. Lastly, the proposed solution is tested and evaluated in multiple real 3D LiDAR sensor recordings taken in an indoor environment. The results show great potential, with particularly high confidence in positive classifications of the human body as compared to state-of-the-art approaches.
激光雷达（LiDAR）技术现在正成为自动驾驶和人机协作等许多应用的主要工具。基于点云的 3D 目标检测由于其在挑战性环境下对相机的有效性，在工业和日常生活中变得越来越流行和广泛接受。在本文中，我们提出了一种使用 3D LiDAR 传感器检测、跟踪和分类人员的模块化方法。它结合了多个原理：用于对象分割的稳健实现、具有局部几何描述符的分类器以及跟踪解决方案。此外，我们通过通过运动检测和运动预测来获取和预测感兴趣区域，从而减少要处理的点的数量，实现了低性能机器的实时解决方案，而无需对环境有任何先验知识。此外，由于传感器视场的限制或极端姿势变化（如蹲下、跳跃和伸展），我们的原型即使在具有挑战性的情况下也能够成功地持续检测和跟踪人员。最后，在所提出的解决方案在室内环境中使用多个真实的 3D LiDAR 传感器记录进行了测试和评估。结果表明具有很大的潜力，与最先进的方法相比，对人体的阳性分类具有特别高的置信度。
