Event-aided Direct Sparse Odometry


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.


If you use this work in your research, please cite the following paper:


J. Hidalgo-Carrió, G.Gallego, D. Scaramuzza

Event-aided Direct Sparse Odometry

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Oral Presentation.

PDF YouTube Code Dataset

Code: EDS_CI

Dataset: High Quality Events & RGB BeamSplitter Sequences for Visual Inertial Odometry

00 peanuts dark

01 peanuts light

02 rocket earth light

03 rocket earth dark

04 floor loop

05 rpg building

06 ziggy and fuzz

07 ziggy and fuzz hdr

08 peanuts running

09 ziggy flying pieces

10 office

11 all characters

12 floor eight loop

13 airplane

14 ziggy in the arena

15 apartment day


00 calib

01 calib

02 hand eye vrpn

03 hand eye vicon