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The Probabilistic Normal Epipolar Constraint for Frame- To-Frame Rotation Optimization under Uncertain Feature Positions

Muhle D, Koestler L, Demmel N, Bernard F, Cremers D (2022)

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Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 1809-1818

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: New Orleans, LA, USA

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.00186

Abstract

The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar constraint (NEC). However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame. In this work, we introduce the probabilistic normal epipolar constraint (PNEC) that overcomes this limitation by accounting for anisotropic and inhomogeneous uncertainties in the feature positions. To this end, we propose a novel objective function, along with an efficient optimization scheme that effectively minimizes our objective while maintaining real-time performance. In experiments on synthetic data, we demonstrate that the novel PNEC yields more accurate rotation estimates than the original NEC and several popular relative rotation estimation algorithms. Furthermore, we integrate the proposed method into a state-of-the-art monocular rotation-only odometry system and achieve consistently improved results for the real-world KITTI dataset.

Involved external institutions

How to cite

APA:

Muhle, D., Koestler, L., Demmel, N., Bernard, F., & Cremers, D. (2022). The Probabilistic Normal Epipolar Constraint for Frame- To-Frame Rotation Optimization under Uncertain Feature Positions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1809-1818). New Orleans, LA, USA: IEEE Computer Society.

MLA:

Muhle, Dominik, et al. "The Probabilistic Normal Epipolar Constraint for Frame- To-Frame Rotation Optimization under Uncertain Feature Positions." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 1809-1818.

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