Toward Event-Based State Estimation for Neuromorphic Event Cameras

In this article, 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 minimum mean squared error (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.