Mutual localization plays a crucial role in multi-robot cooperation. CREPES, a novel system that focuses on six degrees of freedom (DOF) relative pose estimation for multi-robot systems, is proposed in this paper. CREPES has a compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera, an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By leveraging IR light communication, the system solves data association between visual detection and UWB ranging. Ranging measurements from the UWB and directional information from the camera offer relative 3-DOF position estimation. Combining the mutual relative position with neighbors and the gravity constraints provided by IMUs, we can estimate the 6-DOF relative pose from a single frame of sensor measurements. In addition, we design an estimator based on the error-state Kalman filter (ESKF) to enhance system accuracy and robustness. When multiple neighbors are available, a Pose Graph Optimization (PGO) algorithm is applied to further improve system accuracy. We conduct enormous experiments to demonstrate CREPES' accuracy between robot pairs and a team of robots, as well as performance under challenging conditions.
CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System
State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University（工业控制技术国家重点实验室，系统与控制研究所，浙江大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；University of Michigan（密歇根大学）；The Hong Kong Polytechnic University（香港理工大学）
Prioritized Planning for Cooperative Range-Only Localization in Multi-Robot Networks
Massachusetts Institute of Technology（麻省理工学院）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）
我们提出了一种新的路径规划算法，以减少定位误差的机器人网络合作定位通过机器人间的距离测量。利用距离测量进行定位的质量取决于网络的配置，并且差的配置可能导致大量的定位误差。为了减少网络配置对移动网络的定位误差的影响，我们考虑了Fisher信息矩阵（Fisher information matrix，简称HMF）的各种最优性测度，这些测度与定位误差之间的关系已经得到了很好的研究。特别是，我们提出了一个轨迹规划问题的约束条件的最优措施。通过约束这些最优性度量，我们可以控制定位误差的统计特性。为了有效地生成满足这些约束的轨迹，我们提出了一个优先级规划器，它利用基于图形的规划和独特的属性的范围内只有约束。我们在模拟实验中显示的结果表明，我们的算法生成的轨迹减少最坏情况下的定位误差高达42%相比，现有的规划方法，并可以可扩展地规划大量的机器人在复杂的环境中的距离有效的轨迹。
Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
Graduate School of Information Science, Nara Institute of Science and Technology (NAIST)（奈良科学技术大学信息科学研究生院）；R-Frontier Division, Frontier Research Center, Toyota Motor Corporation（丰田汽车公司前沿研究中心R前沿部）
Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM
School of Data Science, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）数据科学学院）；System Hub, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）系统枢纽）
Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization
A General Framework for Multi-vehicle Cooperative Localization Using Pose Graph
Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院科研联盟）；National University of Singapore（新加坡国立大学）；Massachusetts Institute of Technology（麻省理工学院）
SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes
Mechanical and Mechatronics Eng. Department, University of Waterloo（机械与机电工程系，滑铁库大学）；Department of Data and Systems Engineering, University of Hong Kong（数据与系统工程系，香港大学）；Mechanical Engineering Department, University of Alberta（机械工程系，阿尔伯塔大学）
Fusion of Visual-Inertial Odometry with LiDAR Relative Localization for Cooperative Guidance of a Micro-Scale Aerial Vehicle
Multi-robot Systems Group, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technick\'a 2, Prague, 166 27, Czech Republic
RoboDuet: Learning a Cooperative Policy for Whole-body Legged Loco-Manipulation
Tsinghua University（清华大学）；University of San Diego（圣地亚哥大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Jianghuai Advance Technology Center（江淮先进技术中心）
Vision-Based Leader-Follower Formation Control for Cooperative UAVs in GPS-Degraded Environments
Autonomous Robotics Systems Limited（自主机器人系统有限公司）；University of Hyderabad（海得拉巴大学）；Ideabytes Software India Private Limited（Ideabytes软件印度私人有限公司）；Georgia Institute of Technology（佐治亚理工学院）；Tata Consultancy Services（塔塔咨询服务公司）；University of Malaya（马来亚大学）
Decentralized and Fully Onboard: Range-Aided Cooperative Localization and Navigation on Micro Aerial Vehicles
Learning Systems and Robotics Lab（学习系统与机器人实验室）；Technical University of Munich（慕尼黑技术大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Vector Institute for Artificial Intelligence（人工智能向量研究所）
Relative Pose Estimation for Nonholonomic Robot Formation with UWB-IO Measurements (Extended version)
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Cavendish Laboratory, University of Cambridge（剑桥大学卡文迪许实验室）；Zhongguancun Laboratory（中关村实验室）
SPARK: Low Latency Single-Camera 3D Pose Estimation for Autonomous Racing using Keypoints
Technical University of Munich（慕尼黑工业大学）；School of Engineering & Design, Department of Mobility Systems Engineering, Institute of Automotive Technology（工程与设计学院，移动系统工程系，汽车技术研究所）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
CoVOR-SLAM: Cooperative SLAM using Visual Odometry and Ranges for Multi-Robot Systems
Institute of Communications and Navigation, German Aerospace Center (DLR)（德国航空航天中心通信与导航研究所）；Institute for Communications and Navigation, Technical University of Munich（慕尼黑工业大学通信与导航研究所）
Cooperative Integrated Estimation-Guidance for Simultaneous Interception of Moving Targets
Intelligent Systems & Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay（智能系统与控制实验室，航空航天工程系，印度理工学院班加罗尔）；Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati（智能系统引导、自主、学习与控制实验室，航空航天工程与工程力学系，辛辛那提大学）
