Autonomous 3D acquisition of outdoor environments poses special challenges. Different from indoor scenes, where the room space is delineated by clear boundaries and separations (e.g., walls and furniture), an outdoor environment is spacious and unbounded (thinking of a campus). Therefore, unlike for indoor scenes where the scanning effort is mainly devoted to the discovery of boundary surfaces, scanning an open and unbounded area requires actively delimiting the extent of scanning region and dynamically planning a traverse path within that region. Thus, for outdoor scenes, we formulate the planning of an energy-efficient autonomous scanning through a discrete-continuous optimization of robot scanning paths. The discrete optimization computes a topological map, through solving an online traveling sales problem (Online TSP), which determines the scanning goals and paths on-the-fly. The dynamic goals are determined as a collection of visit sites with high reward of visibility-to-unknown. A visit graph is constructed via connecting the visit sites with edges weighted by traversing cost. This topological map evolves as the robot scans via deleting outdated sites that are either visited or become rewardless and inserting newly discovered ones. The continuous part optimizes the traverse paths geometrically between two neighboring visit sites via maximizing the information gain of scanning along the paths. The discrete and continuous processes alternate until the traverse cost of the current graph exceeds the remaining energy capacity of the robot. Our approach is evaluated with both synthetic and field tests, demonstrating its effectiveness and advantages over alternatives. The project is at http://vcc.szu.edu.cn/research/2020/Husky, and the codes are available at https://github.com/alualu628628/Autonomous-Outdoor-Scanning-via-Online-Topological-and-Geometric-Path-Optimization.
CausalNav: A Long-term Embodied Navigation System for Autonomous Mobile Robots in Dynamic Outdoor Scenarios
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University（人工智能与机器人中心，深圳国际研究生院，清华大学）；Peng Cheng Laboratory（鹏城实验室）
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
Technical University of Munich（技术大学慕尼黑）；School of Computation, Information and Technology（计算、信息与技术学院）；Mobile Robotics Lab（移动机器人实验室）；Department of Computing（计算学院）；Imperial College London（伦敦帝国学院）；Department of Mechanical and Process Engineering（机械与过程工程学院）；ETH Zurich（苏黎世联邦理工学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Motion-Compensated Autonomous Scanning for Tumour Localisation using Intraoperative Ultrasound
The Hamlyn Centre for Robotic Surgery, Imperial College London, UK（帝国理工学院哈姆林机器人外科中心）；Department of Surgery and Cancer, Imperial College London, UK（帝国理工学院外科与癌症系）
Real-World Deployment of Cloud-based Autonomous Mobility Systems for Outdoor and Indoor Environments
Department of Mechanical and Mechatronics Engineering, University of Waterloo（滑铁卢大学机械与机电工程系）；Technology Partnerships and Innovations, Rogers Communications, Canada Inc.（罗杰斯通讯加拿大有限公司技术伙伴关系与创新部）；Mechanical Engineering Department, University of Alberta（阿尔伯塔大学机械工程系）
Autonomous Driving in Unstructured Environments: How Far Have We Come?
Research Center for Intelligent Computing Systems, SKLP, Institute of Computing Technology, Chinese Academy of Sciences（智能计算系统研究中心、SKLP、计算技术研究所、中国科学院）；Harbin Engineering University（哈尔滨工程大学）；Jianghuai Advance Technology Center, Anhui Provincial Key Laboratory of Humanoid Robot, Anhui Provincial Industry Innovation Center of Humanoid Robot（江淮先进技术中心、安徽省人形机器人重点实验室、安徽省人形机器人产业创新中心）；Beihang University（北航）；Test Center, National University of Defense Technology（测试中心、国防科技大学）；National University of Defense Technology（国防科技大学）；Unmanned Systems Technology Research Center, Defense Innovation Institute（无人系统技术研究中心、创新研究院）
Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments
University College London（伦敦大学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统前沿科学中心）
A Ground Mobile Robot for Autonomous Terrestrial Laser Scanning-Based Field Phenotyping
School of Electrical and Computer Engineering（电气与计算机工程学院）；The University of Georgia（佐治亚大学）；Bio-Sensing, Automation and Intelligence Laboratory（生物传感、自动化与智能实验室）；Agricultural & Biological Engineering（农业与生物工程）；The University of Florida（佛罗里达大学）
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
The University of Hong Kong（香港大学）；Shanghai AI Laboratory（上海人工智能实验室）；Huawei Noah’s Ark Lab（华为诺亚实验室）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）
FlyCo: Foundation Model-Empowered Drones for Autonomous 3D Structure Scanning in Open-World Environments
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）；Differential Robotics, Hangzhou, China（杭州差分机器人）
Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning
Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
LiDAR Based Semantic Perception for Forklifts in Outdoor Environments
Faculty of Engineering and Informatics, University of Applied Sciences Aschaffenburg（应用科学阿施芬堡大学工程与信息学院）；Linde Material Handling GmbH, Kion Group（林德物料搬运有限公司，Kion集团）
Path Planning and Obstacle Avoidance Scheme for Autonomous Robots using Raspberry Pi
University of Moratuwa（莫拉图瓦大学）
随着机器人平台的逐步发展，使手动过程自动化，路径规划已成为一个关键领域，无论是否了解室内和室外环境。算法可以是智能的或预结构化的，并且应该最有效地到达目的地。在这一领域的主要挑战是找到一条路径，这是免费的静态障碍以及动态障碍。在本文中，提出了一种方法与机器人平台的实施细节，以覆盖关键点，并在动态环境中到达原来的关键点。主要的计算发生在Raspberry Pi B+模块中，指南针，车轮编码器和超声波传感器用于实现机器人到相关关键点的定位。
Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization
Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程研究生院）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程学部）；Technical University of Cartagena（卡塔赫纳技术大学）
The GOOSE Dataset for Perception in Unstructured Environments
Institute for Autonomous Systems Technology, University of the Bundeswehr Munich（Bundeswehr Munich大学自主系统技术研究所）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation（弗劳恩霍夫光学与系统技术及图像利用研究所）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄技术大学）
