Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Nick Weidner

dblp:203/5072 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 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 · 61% Robot navigation and mapping · 39%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.312017
Underwater cave mapping using stereo vision · ICRA 2017
Computer vision › 3D vision › 3d reconstruction › multi-view stereo
stereo reconstruction
0.312017
Underwater cave mapping using stereo vision · ICRA 2017
Robotics › Robot navigation and mapping
visual odometry
0.112017
Underwater cave mapping using stereo vision · ICRA 2017

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

video-light cone intersection · 0.3stereo vision · 0.3
YearPublicationVenuePosition
2017 Underwater cave mapping using stereo vision
abstract
This paper presents a systematic approach for the 3-D mapping of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology, which has never before been addressed. In addition to the underwater vision constraints, cave mapping presents extra challenges in the form of lack of natural illumination and harsh contrasts, resulting in failure for most of the state-of-the-art visual based state estimation packages. A new approach employing a stereo camera and a video-light is presented. Our approach utilizes the intersection of the cone of the video-light with the cave boundaries: walls, floor, and ceiling, resulting in the construction of a wire frame outline of the cave. Successive frames are combined using a state of the art visual odometry algorithm while simultaneously inferring scale through the stereo reconstruction. Results from experiments at a cave, part of the Sistema Camilo, Quintana Roo, Mexico, validate our approach. The cave wall reconstruction presented provides an immersive experience in 3-D.
Nick Weidner, Sharmin Rahman, Alberto Quattrini Li, Ioannis M. Rekleitis
ICRA1