Demo Data from EDBT 2026 / reviewers in the wild / expert
Pierre Marché
dblp:49/6474
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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 · 67% 3D vision · 33% | |
| Computer networks
1 paper |
Wireless sensing and localization · 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 › robot mapping
environment modeling |
0.0 | 1 | 2001 | Quick Primitives Extraction Using Inertia Matrix on Measures Issue from an Ultrasonic Network · ICRA 2001 |
Robotics › Robot navigation and mapping
map building |
0.0 | 1 | 2001 | Quick Primitives Extraction Using Inertia Matrix on Measures Issue from an Ultrasonic Network · ICRA 2001 |
Computer vision › 3D vision › 3d scene understanding
primitive detection |
0.0 | 1 | 2001 | Quick Primitives Extraction Using Inertia Matrix on Measures Issue from an Ultrasonic Network · ICRA 2001 |
Wireless sensing and localization › acoustic sensing
ultrasonic sensing |
0.0 | 1 | 2001 | Quick Primitives Extraction Using Inertia Matrix on Measures Issue from an Ultrasonic Network · ICRA 2001 |
Methods — techniques the papers use, named apart from their topics
inertia matrix · 0.1hough transform · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Quick Primitives Extraction Using Inertia Matrix on Measures Issue from an Ultrasonic NetworkabstractAn autonomous mobile robot needs externals sensors for its own localization and modeling environment. In order to perform the angular precision of ultrasonic sensor and to improve the number of informations for a given time, we developed an ultrasonic sensor network using the multiple receivers mode. In this paper we introduce the ultrasonic network with its hardware architecture and its functioning mode. We also present a method to localize an echo source with the multiple receivers mode and show how to simply model this echo in the robot frame. We then describe the extraction of environment primitives (segment) by the inertia matrix and Hough transform. Finally, we validate the all process by experiments. Joseph Canou, Cyril Novales, Gérard Poisson, Pierre Marché |
ICRA | 4 |
| 1999 | Geodesic Path Based Interpolation Using Level Sets Propagation
Bruno Migeon, Fabien Boissé, Philippe Deforge, Pierre Marché |
CAIP | 4 |