Direct sparse odometry with rolling shutter

David Schubert, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

5 Zitate (Scopus)

Abstract

Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method that incorporates a rolling-shutter model. Our approach extends direct sparse odometry which performs direct bundle adjustment of a set of recent keyframe poses and the depths of a sparse set of image points. We estimate the velocity at each keyframe and impose a constant-velocity prior for the optimization. In this way, we obtain a near real-time, accurate direct VO method. Our approach achieves improved results on challenging rolling-shutter sequences over state-of-the-art global-shutter VO.

OriginalspracheEnglisch
TitelComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
Redakteure/-innenVittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert
Herausgeber (Verlag)Springer Verlag
Seiten699-714
Seitenumfang16
ISBN (Print)9783030012366
DOIs
PublikationsstatusVeröffentlicht - 2018
Veranstaltung15th European Conference on Computer Vision, ECCV 2018 - Munich, Deutschland
Dauer: 8 Sept. 201814 Sept. 2018

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band11212 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz15th European Conference on Computer Vision, ECCV 2018
Land/GebietDeutschland
OrtMunich
Zeitraum8/09/1814/09/18

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