Demo Data from EDBT 2026 / reviewers in the wild / expert
James Fellers
dblp:60/6671
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2000
—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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
localization |
0.0 | 1 | 2000 | Localization Based on Visibility Sectors using Range Sensors · ICRA 2000 |
Robotics › Robot navigation and mapping › localization
range-based localization |
0.0 | 1 | 2000 | Localization Based on Visibility Sectors using Range Sensors · ICRA 2000 |
Methods — techniques the papers use, named apart from their topics
geometric visibility computation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2000 | Localization Based on Visibility Sectors using Range SensorsabstractPresents a rapid localization method for mobile robots. Localization, i.e., absolute position measurement, is an important issue since odometer errors render it impossible for any robot to precisely follow a specified trajectory, resulting in a growing difference between the actual configuration and the calculated configuration as the robot travels. Periodic localization is required to correct these errors. We propose a localization method using range sensor data which is based on simple geometric properties of the environment. In many common situations, information regarding the environment is provided a priori for path planning. During processing, the method proposed here utilizes this information to partition the workspace into sectors using simple visibility computations, and a small identifying label is computed for each sector. The localizer analyzes range sensor readings (distances) and extracts characteristic points, which are compared with the pre-computed sector labels to localize the robot, first to a sector, and then to a particular configuration within that sector. Advantages of this two step process are that it is computationally very simple, and that it allows precise localization without any landmarks from any configuration in the environment. This localization method also provides opportunities for the global navigation procedure to analyze and select trajectories in terms of their tolerance to localization errors. Sooyong Lee, Nancy M. Amato, James Fellers |
ICRA | 3 |