Semi-independent stereo visual odometry for different field of view cameras

Trong Phuc Truong, Vincent Nozick, Hideo Saito

研究成果: Conference contribution

抄録

This paper presents a pipeline for stereo visual odometry using cameras with different fields of view. It gives a proof of concept about how a constraint on the respective field of view of each camera can lead to both an accurate 3D reconstruction and a robust pose estimation. Indeed, when considering a fixed resolution, a narrow field of view has a higher angular resolution and can preserve image texture details. On the other hand, a wide field of view allows to track features over longer periods since the overlap between two successive frames is more substantial. We propose a semi-independent stereo system where each camera performs individually temporal multi-view optimization but their initial parameters are still jointly optimized in an iterative framework. Furthermore, the concept of lead and follow camera is introduced to adaptively propagate information between the cameras. We evaluate the method qualitatively on two indoor datasets, and quantitatively on a synthetic dataset to allow the comparison across different fields of view.

本文言語English
ホスト出版物のタイトルComputer Vision – ECCV 2018 Workshops, Proceedings
編集者Laura Leal-Taixé, Stefan Roth
出版社Springer Verlag
ページ430-442
ページ数13
ISBN(印刷版)9783030110086
DOI
出版ステータスPublished - 2019
イベント15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
継続期間: 2018 9 82018 9 14

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11129 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference15th European Conference on Computer Vision, ECCV 2018
CountryGermany
CityMunich
Period18/9/818/9/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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