Demo Data from EDBT 2026 / reviewers in the wild / expert
Klaus G. Moesl
dblp:95/3640
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
localization |
0.1 | 1 | 2007 | 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 |
0.1 | 1 | 2007 | Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain · ICRA 2007 |
Robotics › Robot navigation and mapping › localization
position estimation |
0.1 | 1 | 2007 | Map Matching Scheme for Position Estimation of Planetary Explorer in Natural Terrain · ICRA 2007 |
Computer vision › 3D vision
stereo vision |
0.1 | 1 | 2007 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Map Matching Scheme for Position Estimation of Planetary Explorer in Natural TerrainabstractThis 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 |
ICRA | 2 |