Asynchronous Optimisation for Event-based Visual Odometry

• URL: http://arxiv.org/abs/2203.01037v1
• Date: Wed, 2 Mar 2022 11:28:47 GMT
• Title: Asynchronous Optimisation for Event-based Visual Odometry
• Authors: Daqi Liu, Alvaro Parra, Yasir Latif, Bo Chen, Tat-Jun Chin and Ian Reid
• Abstract summary: Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range. We focus on event-based visual odometry (VO) We propose an asynchronous structure-from-motion optimisation back-end.
• Score: 53.59879499700895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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