Towards Event-Based State Estimation for Neuromorphic Event Cameras

作者：Xinhui Liu, Meiqi Cheng, Dawei Shi, Ling Shi · 发表于：IEEE Transactions on Automatic Control · 年份：2022 · DOI：10.1109/tac.2022.3202839 · 被引用次数：2 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Age of Information Optimization

In this work, a dynamic information extraction problem for neuromorphic event cameras is investigated from a state estimation perspective. The ego-motion pose estimation task of an event camera is formulated as a state estimation problem for a finite-state hidden Markov model subject to a special event-triggering mechanism. We model the threshold mismatch and the bandwidth limit of the event camera output generalization process as a stochastic event-triggering condition equipped with a state-dependent packet dropout process. For this problem, the recursive expression of the system state conditioned on the event-triggered measurement information is constructed under a suitably designed reference probability measure, based on which the event-based MMSE estimate for the considered estimation problem is further obtained. The effectiveness of proposed results is illustrated by numerical analysis and comparative evaluation of an ego-motion pose estimation example.