Fast odometry and scene flow from RGB-D cameras based on geometric clustering

Jaimez M, Kerl C, Gonzalez-Jimenez J, Cremers D (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Proceedings - IEEE International Conference on Robotics and Automation Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 3992-3999

Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation

Event location: Singapore, SGP

ISBN: 9781509046331

DOI: 10.1109/ICRA.2017.7989459

Abstract

In this paper we propose an efficient solution to jointly estimate the camera motion and a piecewise-rigid scene flow from an RGB-D sequence. The key idea is to perform a two-fold segmentation of the scene, dividing it into geometric clusters that are, in turn, classified as static or moving elements. Representing the dynamic scene as a set of rigid clusters drastically accelerates the motion estimation, while segmenting it into static and dynamic parts allows us to separate the camera motion (odometry) from the rest of motions observed in the scene. The resulting method robustly and accurately determines the motion of an RGB-D camera in dynamic environments with an average runtime of 80 milliseconds on a multi-core CPU. The code is available for public use/test.

Involved external institutions

Universidad de Málaga / University of Malaga ES Spain (ES) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Jaimez, M., Kerl, C., Gonzalez-Jimenez, J., & Cremers, D. (2017). Fast odometry and scene flow from RGB-D cameras based on geometric clustering. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3992-3999). Singapore, SGP: Institute of Electrical and Electronics Engineers Inc..

MLA:

Jaimez, Mariano, et al. "Fast odometry and scene flow from RGB-D cameras based on geometric clustering." Proceedings of the 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, SGP Institute of Electrical and Electronics Engineers Inc., 2017. 3992-3999.

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