SE(3) multimotion estimation through occlusion
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- Publication Date
- Abstract
Visual motion estimation is an integral and well- studied challenge in autonomous navigation. It is significantly more challenging in highly dynamic environments with multiple moving objects. This paper introduces an approach to this multimotion estimation problem capable of estimating the full SE(3) trajectory of every motion in a scene, even when motions become occluded. The Multimotion Visual Odometry (MVO) pipeline employs multilabeling techniques and continuous motion models to estimate all motions simultaneously, including the camera egomotion. Motion closure is used to recognize when trajectories become unoccluded, and the motion models are used to interpolate the occluded estimates. The estimation performance of the pipeline is demonstrated on real-world trajectory data from the Oxford Multimotion Dataset.
- Publication Details
- Type
- Abstract-Refereed Conference Paper
- Conference
- Long-Term Human Motion Prediction (LHMP) Workshop, IEEE International Conference on Robotics and Automation (ICRA)
- Location
- Montréal, QC, Canada
- Manuscript
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- BibTeX Entry
@inproceedings{judd_lhmp19,
author = {Kevin M Judd and Jonathan D Gammell},
title = {{SE(3)} multimotion estimation through occlusion},
booktitle = {Proceedings of the Long-Term Human Motion Prediction ({LHMP}) Workshop, {IEEE} International Conference on Robotics and Automation ({ICRA})},
year = {2019},
address = {Montréal, QC, Canada},
month = {24 } # may,
}