Demo Data from SIGIR 2026 / reviewers in the wild / expert
Dino Hullmann
dblp:203/4827 · also Dino Hüllmann
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 |
Legged, aerial and field robots · 67% Robot navigation and mapping · 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 › Legged, aerial and field robots
aerial robots |
0.3 | 1 | 2017 | Bringing Mobile Robot Olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localization · ICRA 2017 |
Robotics › Legged, aerial and field robots
field robotics |
0.3 | 1 | 2017 | Bringing Mobile Robot Olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localization · ICRA 2017 |
Robotics › Robot navigation and mapping › mobile robot perception
mobile robot olfaction |
0.3 | 1 | 2017 | Bringing Mobile Robot Olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localization · ICRA 2017 |
Wireless sensing and localization
source localization |
0.1 | 1 | 2017 | Bringing Mobile Robot Olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localization · ICRA 2017 |
Methods — techniques the papers use, named apart from their topics
tunable diode laser absorption spectroscopy · 0.6tomographic reconstruction · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Bringing Mobile Robot Olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localizationabstractThis paper introduces a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance. No direct contact with the target gas is needed and thus the influence of the aerial platform on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we introduce and present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile hexacopter. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing the gas sensing and aiming capabilities under realistic conditions. Patrick P. Neumann, Harald Kohlhoff, Dino Hullmann, Achim J. Lilienthal, Martin Kluge |
ICRA | 3 |