The development of aerial autonomy has enabled aerial robots to fly agilely in complex environments. However, dodging fast-moving objects in flight remains a challenge, limiting the further application of unmanned aerial vehicles (UAVs). The bottleneck of solving this problem is the accurate perception of rapid dynamic objects. Recently, event cameras have shown great potential in solving this problem. This paper presents a complete perception system including ego-motion compensation, object detection, and trajectory prediction for fast-moving dynamic objects with low latency and high precision. Firstly, we propose an accurate ego-motion compensation algorithm by considering both rotational and translational motion for more robust object detection. Then, for dynamic object detection, an event camera-based efficient regression algorithm is designed. Finally, we propose an optimizationbased approach that asynchronously fuses event and depth cameras for trajectory prediction. Extensive real-world experiments and benchmarks are performed to validate our framework. Moreover, our code will be released to benefit related researches.
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
Institute for Transport Studies (ITS), University of Leeds（利兹大学交通研究所）；Lincoln Centre for Autonomous Systems, University of Lincoln（林肯大学林肯自主系统中心）；Institute of Engineering Sciences, De Montfort University（德蒙福特大学工程科学研究所）；School of Mechanical Engineering, Nat. Tech. University of Athens（雅典国家技术大学机械工程学院）；Robert Bosch GmbH（罗伯特·博世有限公司）；Ibex Automation Ltd（伊贝克斯自动化有限公司）
Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Department of Electrical and Computer Engineering, Drexel University（电子与计算机工程系，德雷塞尔大学）；GRASP Lab, University of Pennsylvania（GRASP实验室，宾夕法尼亚大学）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（机器人与智能机器研究所，佐治亚理工学院）；Department of Computer Science, University of Southern California（计算机科学系，南加州大学）
D3RoMa: Disparity Diffusion-based Depth Sensing for Material-Agnostic Robotic Manipulation
CFCS；School of Computer Science, Peking University（北京大学计算机学院）；Galbot；University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Observability-driven Assignment of Heterogeneous Sensors for Multi-Target Tracking
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech)（广东省全驱动系统控制理论与技术重点实验室，自动化与智能制造学院，南方科技大学）
Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qizhi Institute（上海期智研究院）；Department of Mechanical Engineering, Tsinghua University（清华大学机械工程系）；Northwestern Polytechnical University（西北工业大学）；Center on Frontiers of Computing Studies, Peking University（北京大学计算与前沿研究中心）；Shanghai AI Laboratory（上海人工智能实验室）
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising
Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（上海交通大学医疗机器人研究所和自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education, Shang hai 200240, China（教育部系统控制与信息处理重点实验室，上海200240，中国）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
National University of Singapore（国立新加坡大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）；Queen Mary University of London（伦敦女王玛丽大学）；The University of Hong Kong（香港大学）
