Event-aided Direct Sparse Odometry

TL;DR

The paper presents EDS, a novel direct monocular visual odometry method using events and frames, capable of accurate 6-DOF tracking at low frame rates, suitable for low-power applications.

Contribution

Introduces EDS, the first direct 6-DOF visual odometry method using events and frames, addressing appearance changes and enabling low-frame-rate operation.

Findings

Works at lower frame rates than state-of-the-art solutions

Provides semi-dense 3D mapping with photometric bundle adjustment

Achieves accurate camera motion tracking using event data

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.