RATE: Real-time Asynchronous Feature Tracking with Event Cameras

Abstract

Vision-based self-localization is a crucial technology for enabling autonomous robot navigation in GPS-deprived environments. However, standard frame cameras are subject to motion blur and suffer from a limited dynamic range. This research focuses on efficient feature tracking for self-localization by using event-based cameras. Such cameras do not provide regular snapshots of the environment but asynchronously collect events that correspond to a small delta of illumination in each pixel independently, thus addressing the issue of motion blur during fast motion and high dynamic range. Specifically, we propose a continuous real-time asynchronous event-based feature tracking pipeline, named RATE. This pipeline integrates (i) a corner detector node utilizing a time slice of the Surface of Active Events to initialize trackers continuously, along with (ii) a tracker node with a proposed "tracking manager", consisting of a grid-based distributor to reduce redundant trackers and to remove feature tracks of poor quality. Evaluations using public datasets reveal that our method maintains a stable number of tracked features, and performs real-time tracking efficiently while maintaining or even improving tracking accuracy compared to state-of-the-art event-only tracking methods.