Kavli Affiliate: Yi Zhou
| First 5 Authors: Sheng Zhong, Sheng Zhong, , ,
| Summary:
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.
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