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Estimation, Search, and Planning (ESP) Research Group

Occlusion-robust MVO: Multimotion estimation through occlusion via motion closure

Authors
  1. Kevin M. Judd
  2. Jonathan D. Gammell
Publication Date
Abstract

Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion.

Previous work in object tracking focuses on maintaining the integrity of object tracks but usually relies on specific appearance-based descriptors or constrained motion models. These approaches are very effective in specific applications but do not generalize to the full multimotion estimation problem.

This paper presents a pipeline for estimating multiple motions, including the camera egomotion, in the presence of occlusions. This approach uses an expressive motion prior to estimate the SE(3) trajectory of every motion in the scene, even during temporary occlusions, and identify the reappearance of motions through motion closure. The performance of this occlusion-robust multimotion visual odometry (MVO) pipeline is evaluated on real-world data and the Oxford Multimotion Dataset.

VideoVideo
PresentationPresentation
Publication Details
Type
Full-Paper-Refereed Conference Paper
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
arXiv Identifier arXiv
1905.05121 [cs.RO]
Manuscript
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BibTeX Entry
@inproceedings{judd_iros20,
author = {Kevin M Judd and Jonathan D Gammell},
title = {Occlusion-robust {MVO}: {Multimotion} estimation through occlusion via motion closure},
booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
year = {2020},
month = {25--29 } # oct,
}