Regularised Energy Model for Robust Monocular Ego-motion Estimation

Hsiang-Jen Chien, Reinhard Klette

2017

Abstract

For two decades, ego-motion estimation is an actively developing topic in computer vision and robotics. The principle of existing motion estimation techniques relies on the minimisation of an energy function based on re-projection errors. In this paper we augment such an energy function by introducing an epipolar-geometry-derived regularisation term. The experiments prove that, by taking soft constraints into account, a more reliable motion estimation is achieved. It also shows that the implementation presented in this paper is able to achieve a remarkable accuracy comparative to the stereo vision approaches, with an overall drift maintained under 2% over hundreds of metres.

Paper Citation

in Harvard Style

Chien H. and Klette R. (2017). Regularised Energy Model for Robust Monocular Ego-motion Estimation . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 361-368. DOI: 10.5220/0006100303610368

in Bibtex Style

@conference{visapp17,
author={Hsiang-Jen Chien and Reinhard Klette},
title={Regularised Energy Model for Robust Monocular Ego-motion Estimation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={361-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006100303610368},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Regularised Energy Model for Robust Monocular Ego-motion Estimation
SN - 978-989-758-227-1
AU - Chien H.
AU - Klette R.
PY - 2017
SP - 361
EP - 368
DO - 10.5220/0006100303610368