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

Klaus G. Moesl

dblp:95/3640 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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
Robot navigation and mapping · 77% 3D vision · 23%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization
0.112007
Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain · ICRA 2007
Robotics › Robot navigation and mapping › localization › map-based localization
map matching
0.112007
Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain · ICRA 2007
Robotics › Robot navigation and mapping › localization
position estimation
0.112007
Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain · ICRA 2007
Computer vision › 3D vision
stereo vision
0.112007
Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain · ICRA 2007

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

triangle configuration matching · 0.1quaternion displacement calculation · 0.1feature point extraction · 0.1
YearPublicationVenuePosition
2007 Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain
abstract
This paper describes a position identification method to precisely estimate the relative movement onboard of an autonomous explorer in such natural terrain as Moon or Mars. This paper proposes a new map matching scheme that registers the visual information of consecutive robot positions and expresses the robot displacement in a common coordinate system. Local elevation maps of the environment are therefore retrieved from the 3D perception of a stereo camera. The proposed scheme consists of three main steps: feature point extraction from map data, matching of features points using triangle configurations and a voting procedure, and direct calculation of the robot displacement in six degrees of freedom using quaternions. The effectiveness of the proposed method is confirmed by computer simulations with synthetic terrain and experiments with stereo perception.
Takashi Kubota, Klaus G. Moesl, Ichiro Nakatani
ICRA2