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arXiv · 2203.03914

Globally-Optimal Event Camera Motion Estimation

Xin Peng·Yifu Wang·Ling Gao·Laurent Kneip
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Abstract

Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. However, different from traditional frame-based cameras, event cameras measure asynchronous pixel-level brightness changes and return them in a highly discretised format, hence new algorithms are needed. The present paper looks at fronto-parallel motion estimation of an event camera. The flow of the events is modeled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of unwarped events. However, in stark contrast to prior art, we derive a globally optimal solution to this generally non-convex problem, and thus remove the dependency on a good initial guess. Our algorithm relies on branch-and-bound optimisation for which we derive novel, recursive upper and lower bounds for six different contrast estimation functions. The practical validity of our approach is supported by a highly successful application to AGV motion estimation with a downward facing event camera, a challenging scenario in which the sensor experiences fronto-parallel motion in front of noisy, fast moving textures.

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Xin Peng, Yifu Wang, Ling Gao, Laurent Kneip. 2022-03-08. Globally-Optimal Event Camera Motion Estimation. https://doi.org/10.1007/978-3-030-58574-7_4

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