Event-aided Direct Sparse Odometry

• Published in: arXiv.org
• Main Authors: Hidalgo-Carrió, Javier, Gallego, Guillermo, Scaramuzza, Davide
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 24.04.2022
• Subjects: Algorithms; Brightness; Bundle adjustment; Cameras; Computer Science - Computer Vision and Pattern Recognition; Frames (data processing)
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2204.07640

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.