Alexis Stoven-Dubois, Kuntima Kiala Miguel, Aziz Dziri, Bertrand Leroy, Roland Chapuis
备注：
2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
发表信息：
2019 IEEE Intelligent Transportation Systems Conference (ITSC)
For connected vehicles to have a substantial effect on road safety, it is required that accurate positions and trajectories can be shared. To this end, all vehicles must be accurately geolocalized in a common frame. This can be achieved by merging GNSS (Global Navigation Satellite System) information and visual observations matched with a map of geo-positioned landmarks. Building such a map remains a challenge, and current solutions are facing strong cost-related limitations.
We present a collaborative framework for high-definition mapping, in which vehicles equipped with standard sensors, such as a GNSS receiver and a mono-visual camera, update a map of geolocalized landmarks. Our system is composed of two processing blocks: the first one is embedded in each vehicle, and aims at geolocalizing the vehicle and the detected feature marks. The second is operated on cloud servers, and uses observations from all the vehicles to compute updates for the map of geo-positioned landmarks. As the map's landmarks are detected and positioned by more and more vehicles, the accuracy of the map increases, eventually converging in probability towards a null error. The landmarks geo-positions are estimated in a stable and scalable way, enabling to provide dynamic map updates in an automatic manner.
A Survey on Collaborative SLAM with 3D Gaussian Splatting
Institute of System Integration, Le Quy Don Technical University, Hanoi, 10000, Vietnam（越南吕文技术大学系统整合研究所）；Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology（日本先进科学技术研究院先进科学技术研究生院）；University of Engineering and Technology, Vietnam National University, Hanoi, 10000, Vietnam（越南国家大学工程技术大学）
3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
Department of Computer and Software Engineering, Polytechnique Montréal（计算机与软件工程系，蒙特利尔理工学院）；Oxford Robotics Institute, Dept. of Engineering Science, University of Oxford（牛津大学机器人研究所，工程科学系）
A Collaborative Visual SLAM Framework for Service Robots
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Intel Corporation（英特尔公司）
S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
Sun Yat-sen University（中山大学）；South China Agricultural University（华南农业大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
High Definition Map Mapping and Update: A General Overview and Future Directions
State Key Laboratory of Automotive Safety and Energy（汽车安全与节能国家重点实验室）；Center for Intelligent Connected Vehicles and Transportation（智能网联汽车与运输中心）；School of Vehicle and Mobility（车辆与动力学院）；Tsinghua University（清华大学）
Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information
Singapore University of Technology and Design（新加坡科技设计大学）；Southwest University of Science and Technology（西南科技大学）；University of Moratuwa（莫拉图瓦大学）；Nanyang Technological University（南洋理工大学）
AirSimAG: A High-Fidelity Simulation Platform for Air-Ground Collaborative Robotics
School of Aeronautic Science and Engineering, Beihang University（北京航空航天大学航空科学与工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Institution of Unmanned System, Beihang University（北京航空航天大学无人系统研究院）
