Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sami Aitya
dblp:230/7026
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
localization |
0.0 | 1 | 1991 | Real-time vision-based robot localization · ICRA 1991 |
Robotics › Robot navigation and mapping › localization
vision-based localization |
0.0 | 1 | 1991 | Real-time vision-based robot localization · ICRA 1991 |
Robotics › Robot navigation and mapping
landmark matching |
0.0 | 1 | 1991 | Real-time vision-based robot localization · ICRA 1991 |
Robotics › Robot navigation and mapping
visual landmark recognition |
0.0 | 1 | 1991 | Real-time vision-based robot localization · ICRA 1991 |
Methods — techniques the papers use, named apart from their topics
geometric tolerance · 0.0correspondence matching · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Real-time vision-based robot localizationabstractAn algorithm for robot localization using visual landmarks is described. This algorithm determines both the correspondence between observed landmarks (in this case vertical edges in the environment) and a preloaded map, and the location of the robot from those correspondences. The primary advantages of this algorithm are its use of a single geometric tolerance to describe observation error, its ability to recognize ambiguous sets of correspondences, its ability to compute bounds on the error in localization, and its fast execution. The current version of the algorithm has been implemented and tested on a mobile robot system. In several hundred trials the algorithm has not failed, and computes location accurate to within a centimeter in less than half a second.> Sami Aitya, Gregory D. Hager |
ICRA | 1 |