Globally-Optimal Event Camera Motion Estimation
- URL: http://arxiv.org/abs/2203.03914v1
- Date: Tue, 8 Mar 2022 08:24:22 GMT
- Title: Globally-Optimal Event Camera Motion Estimation
- Authors: Xin Peng, Yifu Wang, Ling Gao, Laurent Kneip
- Abstract summary: Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution.
Event cameras measure asynchronous pixel-level changes and return them in a highly discretised format.
- Score: 30.79931004393174
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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