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Traditionally, pose estimation is considered as a two step problem. First,feature correspondences are determined by direct comparison of image patches,or by associating feature descriptors. In a second step, the relative pose andthe coordinates of corresponding points are estimated, most often by minimizingthe reprojection error (RPE). RPE optimization is based on a loss function thatis merely aware of the feature pixel positions but not of the underlying imageintensities. In this paper, we propose a sparse direct method which introducesa loss function that allows to simultaneously optimize the unscaled relativepose, as well as the set of feature correspondences directly considering theimage intensity values. Furthermore, we show how to integrate statistical priorinformation on the motion into the optimization process. This constructiveinclusion of a Bayesian bias term is particularly efficient in applicationcases with a strongly predictable (short term) dynamic, e.g. in a drivingscenario. In our experiments, we demonstrate that the JET algorithm we proposeoutperforms the classical reprojection error optimization on two syntheticdatasets and on the KITTI dataset. The JET algorithm runs in real-time on asingle CPU thread.

Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences