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Jon Zubizarreta

dblp:239/4997 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0003-1107-5131ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 56% Robot navigation and mapping · 44%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › structure from motion
bundle adjustment		 0.412020
Direct Sparse Mapping · IEEE Trans. Robotics 2020
Computer vision › 3D vision › 3d reconstruction
photometric bundle adjustment		 0.412020
Direct Sparse Mapping · IEEE Trans. Robotics 2020
Robotics › Robot navigation and mapping › SLAM
visual SLAM		 0.412020
Direct Sparse Mapping · IEEE Trans. Robotics 2020
Robotics › Robot navigation and mapping
localization		 0.112020
Direct Sparse Mapping · IEEE Trans. Robotics 2020
Robotics › Robot navigation and mapping › SLAM › visual SLAM
monocular SLAM		 0.112020
Direct Sparse Mapping · IEEE Trans. Robotics 2020

Methods — techniques the papers use, named apart from their topics

photometric bundle adjustment · 0.4direct method · 0.4
YearPublicationVenuePosition
2020 Direct Sparse Mapping
abstract
Photometric bundle adjustment (PBA) accurately estimates geometry from video. However, current PBA systems have a temporary map that cannot manage scene reobservations. We present, direct sparse mapping, a full monocular visual simultaneous localization and mapping (SLAM) based on PBA. Its persistent map handles reobservations, yielding the most accurate results up to date on EuRoC for a direct method.
Jon Zubizarreta, Iker Aguinaga, J. M. M. Montiel
 IEEE Trans. Robotics1