IMU-Aided Event-based Stereo Visual Odometry

TL;DR

This paper enhances event-based stereo visual odometry by improving accuracy and efficiency through edge-pixel sampling, combining stereo results, and integrating gyroscope data for better pose tracking, validated on public datasets.

Contribution

It introduces an efficient edge-pixel sampling strategy, combines temporal and static stereo results, and incorporates gyroscope priors to improve event-based visual odometry accuracy and robustness.

Findings

Improved mapping completeness and smoothness.

Enhanced pose tracking accuracy with gyroscope priors.

Validated performance improvements on public datasets.

Abstract

Direct methods for event-based visual odometry solve the mapping and camera pose tracking sub-problems by establishing implicit data association in a way that the generative model of events is exploited. The main bottlenecks faced by state-of-the-art work in this field include the high computational complexity of mapping and the limited accuracy of tracking. In this paper, we improve our previous direct pipeline \textit{Event-based Stereo Visual Odometry} in terms of accuracy and efficiency. To speed up the mapping operation, we propose an efficient strategy of edge-pixel sampling according to the local dynamics of events. The mapping performance in terms of completeness and local smoothness is also improved by combining the temporal stereo results and the static stereo results. To circumvent the degeneracy issue of camera pose tracking in recovering the yaw component of general 6-DoF motion, we introduce as a prior the gyroscope measurements via pre-integration. Experiments on publicly available datasets justify our improvement. We release our pipeline as an open-source software for future research in this field.