Feature-based Event Stereo Visual Odometry

TL;DR

This paper introduces a new stereo visual odometry method for event cameras that improves robustness and adaptability in challenging environments by using feature detection, matching, and reprojection error minimization, outperforming existing methods in outdoor scenarios.

Contribution

The proposed method offers a novel approach to stereo event-based odometry with adaptive tracking and no initialization requirement, demonstrating competitive performance on public datasets.

Findings

Performs on par with ESVO on indoor datasets

Handles outdoor driving scenarios where ESVO fails

Adapts tracking frequency to event rate

Abstract

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on the MVSEC sequences, while on the DSEC dataset ESVO, unlike our method, was unable to handle outdoor driving scenario with default parameters. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization.