Asynchronous Optimisation for Event-based Visual Odometry
Date
2022
Authors
Liu, D.
Parra, A.
Latif, Y.
Chen, B.
Chin, T.J.
Reid, I.
Editors
Advisors
Journal Title
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Volume Title
Type:
Conference paper
Citation
IEEE International Conference on Robotics and Automation, 2022, vol.2022-January, pp.9432-9438
Statement of Responsibility
Daqi Liu, Alvaro Parra, Yasir Latif, Bo Chen, Tat-Jun Chin, Ian Reid
Conference Name
International Conference on Robotics and Automation (ICRA) (23 May 2022 - 27 May 2022 : Philadelphia, PA, United States)
Abstract
— Event cameras open up new possibilities for
robotic perception due to their low latency and high dynamic
Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range. On the other hand, developing effective event-based vision algorithms that fully exploit the beneficial properties of event cameras remains work in progress. In this paper, we focus on event-based visual odometry (VO). While existing event-driven VO pipelines have adopted continuous-time representations to asynchronously process event data, they either assume a known map, restrict the camera to planar trajectories, or integrate other sensors into the system. Towards map-free event-only monocular VO in SE(3), we propose an asynchronous structure-from-motion optimisation back-end. Our formulation is underpinned by a principled joint optimisation problem involving non-parametric Gaussian Process motion modelling and incremental maximum a posteriori inference. A high-performance incremental computation engine is employed to reason about the camera trajectory with every incoming event. We demonstrate the robustness of our asynchronous back-end in comparison to frame-based methods which depend on accurate temporal accumulation of measurements.
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© 2022 IEEE.