Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure and reduce the odometry drift. Image-based place recognition received a lot of attention in computer vision, and in this work, we investigate how such approaches can be used for 3D LiDAR data. Recent LiDAR sensors produce high-resolution 3D scans in combination with comparably stable intensity measurements. Through a cylindrical projection, we can turn this information into a panoramic image. As a result, we can apply techniques from visual place recognition to LiDAR intensity data. The question of how well this approach works in practice has not been answered so far. This paper provides an analysis of how such visual techniques can be with LiDAR data, and we provide an evaluation on different datasets. Our results suggest that this form of place recognition is possible and an effective means for determining loop closures.
State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC)（物联网智能城市国家重点实验室）；Faculty of Science and Technology（科技学院）；University of Macau（澳门大学）；School of Computing and Information Systems（计算与信息学院）；Singapore Management University（新加坡管理大学）；Department of Naval Architecture and Marine Engineering and Department of Robotics（船舶工程与海洋工程系和机器人系）；University of Michigan（密歇根大学）；Department of Robotics（机器人系）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·拉希德智能人工智能大学）
InTEn-LOAM: Intensity and Temporal Enhanced LiDAR Odometry and Mapping
National Innovation Institute of Defence Technology, PLA Academy of Military Sciences（中国人民解放军军事科学院国防科技创新研究院）；PLA Information Engineering University（中国人民解放军信息工程大学）
同时定位与地图构建（Simultaneous Localization And Mapping，SLAM）是一种基于光探测与测距（Light Detection And Ranging，LiDAR）和摄像机等传感器的观测来估计机器人位置并重建环境的任务。它广泛应用于自动驾驶和无人机交付等机器人应用。传统的基于LiDAR的SLAM算法主要利用场景的几何特征，而忽略了LiDAR的亮度信息。最近的一些基于深度学习的SLAM算法考虑强度特征并以端到端的方式训练姿态估计网络。然而，它们需要大量的数据汇集工作，并且它们对除受过训练的环境之外的环境的普遍性仍然不清楚。在本文中，我们引入强度功能的SLAM系统。我们提出了一个新的完整的SLAM框架，利用几何和强度功能。所提出的SLAM涉及基于强度的前端里程估计和基于强度的后端优化。进行了全面的实验，包括室外自动驾驶和室内仓库机器人操作。实验结果表明，该方法优于现有的仅几何LiDAR SLAM方法。
Matched Filtering based LiDAR Place Recognition for Urban and Natural Environments
Queensland University of Technology（昆士兰科技大学）；QUT Centre for Robotics（昆士兰科技大学机器人中心）；School of Electrical Engineering and Robotics（电气工程与机器人学院）；Intel Labs（英特尔实验室）
Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学全球未来技术研究院）
CCTNet: A Circular Convolutional Transformer Network for LiDAR-based Place Recognition Handling Movable Objects Occlusion
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；China North Vehicle Research Institute (NOVERI)（中国北方车辆研究所）；Centre for Composite Materials and Structures, Harbin Institute of Technology（哈尔滨工业大学复合材料与结构中心）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology（特殊环境先进复合材料科学与技术国家重点实验室）
An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather
School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）电子与信息工程学院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）；School of Information Communication Technology, Shenzhen Institute of Information Technology（深圳信息职业技术学院信息通信技术学院）；Harvard AI and Robotics Lab, Harvard University（哈佛大学AI与机器人实验室）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；School of Electronic and Communication Engineering, Shenzhen Polytechnic University（深圳职业技术大学电子与通信工程学院）
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
Technical University of Munich（慕尼黑技术大学）；Wuhan University（武汉大学）；University of Science and Technology of China（中国科学技术大学）；University of Cambridge（剑桥大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；Haomo.AI Technology Co., Ltd（哈莫人工智能技术有限公司）；Ningbo Innovation Center, Zhejiang University（宁波创新中心，浙江大学）；College of Instrument Science and Optoelectronics Engineering, Beihang University（仪器科学与光电工程学院，北航）；College of Information Science and Electronic Engineering, Zhejiang University（信息科学与电子工程学院，浙江大学）
Voronoi-based Second-order Descriptor with Whitened Metric in LiDAR Place Recognition
Interdisciplinary Program in Neuroscience（神经科学跨学科项目）；Département d’Informatique, École Normale Supérieure (ENS)（高等师范学院计算机系）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）；Dept. of Computer Science and Engineering, Seoul National University（首尔国立大学计算机科学与工程系）
Mapping with Reflection -- Detection and Utilization of Reflection in 3D Lidar Scans
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Mobile Autonomous Robotic Systems Lab (MARS Lab)（移动自主机器人系统实验室（MARS实验室））；ShanghaiTech University（上海科技大学）
