Event-aided Direct Sparse Odometry
- URL: http://arxiv.org/abs/2204.07640v1
- Date: Fri, 15 Apr 2022 20:40:29 GMT
- Title: Event-aided Direct Sparse Odometry
- Authors: Javier Hidalgo-Carri\'o and Guillermo Gallego and Davide Scaramuzza
- Abstract summary: We introduce EDS, a direct monocular visual odometry using events and frames.
Our algorithm leverages the event generation model to track the camera motion in the blind time between frames.
EDS is the first method to perform 6-DOF VO using events and frames with a direct approach.
- Score: 54.602311491827805
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce EDS, a direct monocular visual odometry using events and frames.
Our algorithm leverages the event generation model to track the camera motion
in the blind time between frames. The method formulates a direct probabilistic
approach of observed brightness increments. Per-pixel brightness increments are
predicted using a sparse number of selected 3D points and are compared to the
events via the brightness increment error to estimate camera motion. The method
recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the
first method to perform 6-DOF VO using events and frames with a direct
approach. By design, it overcomes the problem of changing appearance in
indirect methods. We also show that, for a target error performance, EDS can
work at lower frame rates than state-of-the-art frame-based VO solutions. This
opens the door to low-power motion-tracking applications where frames are
sparingly triggered "on demand" and our method tracks the motion in between. We
release code and datasets to the public.
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