面向三维点云的场景识别方法综述

Scene recognition for 3D point clouds:a review

基于智能机器人代替人到各种复杂环境完成探测、防疫等大量应用的需求,场景的识别引起了研究者的广泛关注。场景识别的目的是通过提取和分析场景中的特征,获得场景的高层语义信息,从而推理出所处的具体位置,它是同步定位与建图系统(Simultaneous Localization and Mapping,SLAM)、自动驾驶、机器人导航、闭环检测的基础。三维扫描技术的快速发展使得人们能够利用各种扫描仪快速获取各类场景的点云数据。不论扫描时间、光照环境如何变化,点云场景所获取的几何信息都具有较好的不变性,因此,基于点云的场景识别成为计算机视觉领域的研究热点。本文首先对近年来面向点云数据的场景识别方法进行了归纳和总结;然后介绍用于场景识别的大规模室内/室外场景的数据集,以及用于算法评测的评价指标,同时总结了各类算法的识别率。最后指出面向点云的场景识别中所面临的问题和挑战,对未来的研究趋势进行展望。研究结果有助于相关领域学者快速全面地了解基于点云数据场景识别的研究现状,为进一步提升场景识别精度奠定基础。

Intelligent robots can perform several high-risk tasks such as object detection and epidemic prevention to aid human beings. Research on scene recognition has attracted considerable attention in recent years. Scene recognition aims to obtain high-level semantic features and infer the location of a scene,laying a good foundation for simultaneous localization and mapping,autonomous driving,intelligent robotics,and loop detection. With the rapid development of 3D scanning technology,obtaining point clouds of various scenes using various scanners is extremely convenient. Compared with images,the geometric features of point clouds are invariant to drastic lighting and time changes,thus making the process of localization robust. Therefore,scene recognition of point clouds is one of the most important and fundamental research topics in computer vision. This paper systematically expounds the progress and current situation of scene recognition techniques of point clouds,including traditional methods and deep learning methods. Then,several public datasets for scene recognition are introduced in detail. The recognition rates of various algorithms are summarized. Finally,we note the challenges and future research directions of the scene recognition of point clouds. This study will help researchers in related fields to better understand the research status of scene recognition of point clouds quickly and comprehensively and lay a foundation for a further im-provement in the recognition accuracy.