Demo Data from EDBT 2026 / reviewers in the wild / expert
Raaghav Radhakrishnan
dblp:301/8175
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › localization › appearance-based localization
ground texture localization |
0.6 | 1 | 2022 | HD Ground - A Database for Ground Texture Based Localization · ICRA 2022 |
Robotics › Robot navigation and mapping › localization
robot localization |
0.6 | 1 | 2022 | HD Ground - A Database for Ground Texture Based Localization · ICRA 2022 |
Robotics › Robot navigation and mapping › mobile robot navigation › route navigation
teach-and-repeat navigation |
0.2 | 1 | 2022 | HD Ground - A Database for Ground Texture Based Localization · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
benchmark dataset · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | HD Ground - A Database for Ground Texture Based LocalizationabstractWe present the HD Ground Database, a comprehensive database for ground texture based localization. It contains sequences of a variety of textures, obtained using a downward facing camera. In contrast to existing databases of ground images, the HD Ground Database is larger, has a greater variety of textures, and has a higher image resolution with less motion blur. Also, our database enables the first systematic study of how natural changes of the ground that occur over time affect localization performance, and it allows to examine a teach-and-repeat navigation scenario. We use the HD Ground Database to evaluate four state-of-the-art localization approaches for global localization, localization with the approximate pose being known, and relative localization. Jan Fabian Schmid, Stephan F. Simon, Raaghav Radhakrishnan, Simone Frintrop, Rudolf Mester |
ICRA | 3 |