Deep Visual Odometry for Stereo Event Cameras

Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HDR conditions, where the dynamic range can be enormous and the signal-to-noise ratio is spatially-and-temporally varying. Leveraging deep neural networks offers new possibilities for overcoming these challenges. In this paper, we propose a learning-based stereo event visual odometry. Building upon Deep Event Visual Odometry (DEVO), our system (called Stereo-DEVO) introduces a novel and efficient static-stereo association strategy for sparse depth estimation with almost no additional computational burden. By integrating it into a tightly coupled bundle adjustment (BA) optimization scheme, and benefiting from the recurrent network's ability to perform accurate optical flow estimation through voxel-based event representations to establish reliable patch associations, our system achieves high-precision pose estimation in metric scale. In contrast to the offline performance of DEVO, our system can process event data of \zs{Video Graphics Array} (VGA) resolution in real time. Extensive evaluations on multiple public real-world datasets and self-collected data justify our system's versatility, demonstrating superior performance compared to state-of-the-art event-based VO methods. More importantly, our system achieves stable pose estimation even in large-scale nighttime HDR scenarios.

ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras

Event-based visual odometry is a specific branch of visual Simultaneous Localization and Mapping (SLAM) techniques, which aims at solving tracking and mapping sub-problems in parallel by exploiting the special working principles of neuromorphic (ie, event-based) cameras. Due to the motion-dependent nature of event data, explicit data association ie, feature matching under large-baseline view-point changes is hardly established, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping sub-problem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this paper, we resolve these issues by building an event-based stereo visual-inertial odometry system on top of our previous direct pipeline Event-based Stereo Visual Odometry. Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general six-DoF motion, we introduce IMU measurements as motion priors via pre-integration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods.

IMU-Aided Event-based Stereo Visual Odometry

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.