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Accurate camera motion estimation is a fundamental building block for manyComputer Vision algorithms. For improved robustness, temporal consistency oftranslational and rotational camera velocity is often assumed by propagatingmotion information forward using stochastic filters. Classical stochasticfilters, however, use linear approximations for the non-linear observer modeland for the non-linear structure of the underlying Lie Group$\operatorname{SE}_3$ and have to approximate the unknown posterioridistribution. In this paper we employ a non-linear measurement model for thecamera motion estimation problem that incorporates multiple observationequations. We solve the underlying filtering problem using a novel MinimumEnergy Filter on $\operatorname{SE}_3$ and give explicit expressions for theoptimal state variables. Experiments on the challenging KITTI benchmark showthat, although a simple motion model is only employed, our approach improvesrotational velocity estimation and otherwise is on par with thestate-of-the-art.

Second Order Minimum Energy Filtering on $\operatorname{SE}_3$ with Nonlinear Measurement Equations