2020/10/06 by William Chamorro, Chamorro, William, Juan Andrade‐Cetto +3 · 3 citations
Computer Science · Engineering · #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks #Underwater Vehicles and Communication Systems
paper · pdf · doi:10.48550/arxiv.2010.02771
openalex publication_date 2020/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work,we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8 g, at a throughput of 10 kHz,processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. The method includes a robust mechanism for the matching of events with projected line segments with very fast outlier rejection. Meticulous treatment of sparse matrices is applied to achieve real-time performance. Different motion models of varying complexity are considered for the sake of comparison and performance analysis