Event-aided Direct Sparse Odometry
| Autor: | Hidalgo-Carrió, Javier, Gallego, Guillermo, Scaramuzza, Davide |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, 2022 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/CVPR52688.2022.00569 |
| Popis: | 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. Comment: 16 pages, 14 Figures, Page: https://rpg.ifi.uzh.ch/eds |
| Databáze: | arXiv |
| Externí odkaz: |
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