IMU-Aided Event-based Stereo Visual Odometry
This work addresses bottlenecks in event-based visual odometry for robotics and autonomous systems, representing an incremental improvement over prior methods.
The paper tackles the high computational complexity and limited accuracy in event-based stereo visual odometry by improving a previous direct pipeline, achieving enhanced accuracy and efficiency through edge-pixel sampling and combining temporal and static stereo results, with experiments on public datasets justifying these improvements.
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.