Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hitoshi Okumura
dblp:84/5090
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1994
—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 · 81% 3D vision · 19% |
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 › mobile robot perception
visual sensing |
0.0 | 1 | 1994 | Multiple Visual Sensing System for Mobile Robot · ICRA 1994 |
Computer vision › 3D vision › motion estimation
ego-motion estimation |
0.0 | 1 | 1994 | Multiple Visual Sensing System for Mobile Robot · ICRA 1994 |
Robotics › Robot navigation and mapping
localization |
0.0 | 1 | 1994 | Multiple Visual Sensing System for Mobile Robot · ICRA 1994 |
Methods — techniques the papers use, named apart from their topics
trinocular vision · 0.0omnidirectional image sensor · 0.0binocular vision · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1994 | Multiple Visual Sensing System for Mobile RobotabstractIn this paper, we propose a new multiple visual sensing sensor (MISS), which combines with an omnidirectional image sensor COPIS (COnic Projection Image Sensor) and binocular vision, for navigating the robot and understanding interesting objects in an environment by integrating both sensory data. Since COPIS observes a 360 degree view around the robot, the robot can always estimate its own location and motion precisely. The location of unknown objects can also be estimated. COPIS can observe a global and precise information of features (vertical edges) in real-time, however, it is difficult to understand their details of shapes. On the other hand, the mobile robot has binocular vision and obtains a sequence of stereo images from the environment. We extend the principle of trinocular vision to establish correspondences between a sequence of binocular images. Although a view field of the binocular vision is limited by a visual angle of lens, the binocular vision is useful for understand spatial configuration of the environment. Therefore we integrate both merits and propose an efficient sensing system. The system has been evaluated on the prototype sensor in actual environment.> Yasushi Yagi, Hitoshi Okumura, Masahiko Yachida |
ICRA | 2 |