Autonomous inspection of powerlines with quadrotors is challenging. Flights require persistent perception to keep a close look at the lines. We propose a method that uses event cameras to robustly track powerlines. Event cameras are inherently robust to motion blur, have low latency, and high dynamic range. Such properties are advantageous for autonomous inspection of powerlines with drones, where fast motions and challenging illumination conditions are ordinary. Our method identifies lines in the stream of events by detecting planes in the spatio-temporal signal, and tracks them through time. The implementation runs onboard and is capable of detecting multiple distinct lines in real time with rates of up to $320$ thousand events per second. The performance is evaluated in real-world flights along a powerline. The tracker is able to persistently track the powerlines, with a mean lifetime of the line $10\times$ longer than existing approaches.
USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways
School of Information Engineering, Yancheng Institute Technology（盐城理工学院信息工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能与智能交通研究部）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进科技学院）；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）
DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects
School of Artificial Intelligence, Sun Yat-Sen University（中山大学人工智能学院）；School of Mechanical Engineering, Xi’an Jiaotong University（西安交通大学机械工程学院）；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）
Design, Implementation and Evaluation of an External Pose-Tracking System for Underwater Cameras
GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）；Christian-Albrechts-Universität zu Kiel（基尔大学）；University of Applied Sciences Kiel（基尔应用科学大学）
