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Harald Kohlhoff

dblp:203/4668 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots
0.312017
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
0.312017
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
0.312017
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
0.112017
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
YearPublicationVenuePosition
2017 Bringing Mobile Robot Olfaction to the next dimension - UAV-based remote sensing of gas clouds and source localization
abstract
This 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
ICRA2