Asynchronous Optimisation for Event-based Visual Odometry

TL;DR

This paper introduces an asynchronous structure-from-motion optimization method for event-based visual odometry that operates in real-time without relying on known maps or additional sensors, leveraging Gaussian Process modeling.

Contribution

It presents a novel asynchronous optimization framework for event-only monocular visual odometry using Gaussian Processes and incremental inference, enabling map-free 3D motion estimation.

Findings

Demonstrates robustness over frame-based methods in dynamic environments

Achieves real-time performance with high event throughput

Operates effectively without prior map or additional sensors

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.