LiDAR has become one of the primary sensors in robotics and autonomous system for high-accuracy situational awareness. In recent years, multi-modal LiDAR systems emerged, and among them, LiDAR-as-a-camera sensors provide not only 3D point clouds but also fixed-resolution 360{\deg}panoramic images by encoding either depth, reflectivity, or near-infrared light in the image pixels. This potentially brings computer vision capabilities on top of the potential of LiDAR itself. In this paper, we are specifically interested in utilizing LiDARs and LiDAR-generated images for tracking Unmanned Aerial Vehicles (UAVs) in real-time which can benefit applications including docking, remote identification, or counter-UAV systems, among others. This is, to the best of our knowledge, the first work that explores the possibility of fusing the images and point cloud generated by a single LiDAR sensor to track a UAV without a priori known initialized position. We trained a custom YOLOv5 model for detecting UAVs based on the panoramic images collected in an indoor experiment arena with a MOCAP system. By integrating with the point cloud, we are able to continuously provide the position of the UAV. Our experiment demonstrated the effectiveness of the proposed UAV tracking approach compared with methods based only on point clouds or images. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform.

翻译：激光雷达已成为机器人和自主系统中高精确度状况意识的主要传感器之一。近年来,出现了多式激光雷达系统,其中,激光雷达系统不仅提供3D点云,而且提供固定分辨率360xdeg}光谱图像,将深度、反射率或近红外光在图像像素中进行编码。这有可能在激光雷达本身的潜力之上带来计算机视觉能力。在本文中,我们特别有兴趣实时利用激光雷达和激光雷达生成图像跟踪无人驾驶的移动飞行器(UAVs),这不但有利于3D点云,而且有利于应用,包括对接接、远程识别或反卫星系统等。根据我们的知识,这是探索将图像和由单一的激光雷达传感器生成的点放大的可能性的首次工作,可以追踪无人驾驶飞行器本身的潜力。我们训练了用于实时跟踪无人驾驶飞行器和激光雷达生成的图像(UAVAVs),用于实时跟踪无人驾驶飞行器(UAVAVs)的图像(UAVS),其位置与我们所展示的直径系统相比,通过一个不断展示的轨道,通过我们所收集的图像,在室内的轨道上进行实验,提供了我们所展示的图像。</s>