Demo Data from EDBT 2026 / reviewers in the wild / expert
Ldyla Cahut
dblp:52/6550
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1998
—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 |
Robot navigation and mapping · 88% Motion planning and robot control · 12% |
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
environment mapping |
0.0 | 1 | 1998 | Sonar Resolution-Based Environment Mapping · ICRA 1998 |
Robotics › Robot navigation and mapping › robot mapping › range-based mapping
sonar-based mapping |
0.0 | 1 | 1998 | Sonar Resolution-Based Environment Mapping · ICRA 1998 |
Robotics › Robot navigation and mapping › obstacle avoidance
collision-free navigation |
0.0 | 1 | 1998 | Sonar Resolution-Based Environment Mapping · ICRA 1998 |
Robotics › Motion planning and robot control
motion planning |
0.0 | 1 | 1998 | Sonar Resolution-Based Environment Mapping · ICRA 1998 |
Methods — techniques the papers use, named apart from their topics
sonar range sensing · 0.0map matching and update · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | Sonar Resolution-Based Environment MappingabstractUltrasonic range sensors are used to obtain the information required for collision-free navigation of a mobile robot in a semi-structured or unstructured environment. A set of range readings from a ring of sonars are correlated to acquire a 2D map of the robot's environment. The map is continuously enhanced via novel matching and update algorithms as new data are collected while the robot is in motion. The algorithm utilizes confidence measures that are directly obtained from the sonar's resolution or accuracy. There are no additional modeling assumptions and the approach is robust. The algorithms are experimentally tested on the Nomad 200 mobile robot and Nomad's Cognos Software Development Package. Ldyla Cahut, Kimon P. Valavanis, Hakan Deliç |
ICRA | 1 |