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Estimation, Search, and Planning (ESP) Research Group

Event-based stereo visual odometry with native temporal resolution via continuous-time Gaussian process regression

Authors
  1. Jianeng Wang
  2. Jonathan D. Gammell
Publication Date
Abstract

Event-based cameras asynchronously capture individual visual changes in a scene. This makes them more robust than traditional frame-based cameras to highly dynamic motions and poor illumination. It also means that every measurement in a scene can occur at a unique time.

Handling these different measurement times is a major challenge of using event-based cameras. It is often addressed in visual odometry (VO) pipelines by approximating temporally close measurements as occurring at one common time. This grouping simplifies the estimation problem but, absent additional sensors, sacrifices the inherent temporal resolution of event-based cameras.

This paper instead presents a complete stereo VO pipeline that estimates directly with individual event-measurement times without requiring any grouping or approximation in the estimation state. It uses continuous-time trajectory estimation to maintain the temporal fidelity and asynchronous nature of event-based cameras through Gaussian process regression with a physically motivated prior. Its performance is evaluated on the MVSEC dataset, where it achieves 7.9⋅10-3 and 5.9⋅10-3 RMS relative error on two independent sequences, outperforming the existing publicly available event-based stereo VO pipeline by two and four times, respectively.

Publication Details
Type
Journal Paper
Journal
IEEE Robotics and Automation Letters (RA-L)
Volume
8
Number
10
Pages
6707–6714
Digital Object IdentifierDOI
10.1109/LRA.2023.3311374
arXiv Identifier
2306.01188 [cs.RO]
Notes
Presented at ICRA 2024
Manuscript
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BibTeX Entry
@article{wang_ral23,
author = {Jianeng Wang and Jonathan D Gammell},
title = {Event-based stereo visual odometry with native temporal resolution via continuous-time {Gaussian} process regression},
journal = {{IEEE} Robotics and Automation Letters ({RA-L})},
year = {2023},
volume = {8},
number = {10},
pages = {6707--6714},
month = oct,
doi = {10.1109/LRA.2023.3311374},
}