Details
| Originalsprache | Englisch |
|---|---|
| Seiten | 30-36 |
| Seitenumfang | 7 |
| Publikationsstatus | Veröffentlicht - 2016 |
| Veranstaltung | 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016 - Lisbon, Portugal Dauer: 29 Juli 2016 → 31 Juli 2016 |
Konferenz
| Konferenz | 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016 |
|---|---|
| Land/Gebiet | Portugal |
| Ort | Lisbon |
| Zeitraum | 29 Juli 2016 → 31 Juli 2016 |
Abstract
LiDAR sensors are unable to detect objects that are inside or behind dense smoke, fog or dust. These aerosols lead to problems for environmental modeling with mobile robotic platforms. For example, if a robot equipped with a LiDAR is surrounded by dense smoke, it can neither localize itself nor can it create a map. Radar sensors, on the other hand, are immune to these conditions, but are unable to represent the structure of an environment in the same quality as a LiDAR due to limited range and angular resolution. In this paper, we introduce the mechanically pivoting radar (MPR), which is a 2D high bandwidth radar scanner. We present first results for robotic mapping and a fusion strategy in order to reduce the negative influence of the aforementioned harsh conditions on LiDAR scans. In addition to the metric representation of an environment with low visibility, we introduce the LRR (LiDAR-Radar-Ratio), which correlates with the amount of aerosols around the robot discussing its meaning and possible application.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Information systems
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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- BibTex
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2016. 30-36 Beitrag in 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016, Lisbon, Portugal.
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - Radar and LiDAR Sensorfusion in Low Visibility Environments
AU - Fritsche, Paul
AU - Kueppers, Simon
AU - Briese, Gunnar
AU - Wagner, Bernardo
N1 - Funding Information: This work has partly been supported within H2020- ICT by the European Commission under grant agreement number 645101 (SmokeBot).
PY - 2016
Y1 - 2016
N2 - LiDAR sensors are unable to detect objects that are inside or behind dense smoke, fog or dust. These aerosols lead to problems for environmental modeling with mobile robotic platforms. For example, if a robot equipped with a LiDAR is surrounded by dense smoke, it can neither localize itself nor can it create a map. Radar sensors, on the other hand, are immune to these conditions, but are unable to represent the structure of an environment in the same quality as a LiDAR due to limited range and angular resolution. In this paper, we introduce the mechanically pivoting radar (MPR), which is a 2D high bandwidth radar scanner. We present first results for robotic mapping and a fusion strategy in order to reduce the negative influence of the aforementioned harsh conditions on LiDAR scans. In addition to the metric representation of an environment with low visibility, we introduce the LRR (LiDAR-Radar-Ratio), which correlates with the amount of aerosols around the robot discussing its meaning and possible application.
AB - LiDAR sensors are unable to detect objects that are inside or behind dense smoke, fog or dust. These aerosols lead to problems for environmental modeling with mobile robotic platforms. For example, if a robot equipped with a LiDAR is surrounded by dense smoke, it can neither localize itself nor can it create a map. Radar sensors, on the other hand, are immune to these conditions, but are unable to represent the structure of an environment in the same quality as a LiDAR due to limited range and angular resolution. In this paper, we introduce the mechanically pivoting radar (MPR), which is a 2D high bandwidth radar scanner. We present first results for robotic mapping and a fusion strategy in order to reduce the negative influence of the aforementioned harsh conditions on LiDAR scans. In addition to the metric representation of an environment with low visibility, we introduce the LRR (LiDAR-Radar-Ratio), which correlates with the amount of aerosols around the robot discussing its meaning and possible application.
KW - FMCW-radar
KW - LiDAR
KW - Low visibility environments
KW - Mobile robotics
KW - Sensorfusion
KW - Smoke detection
KW - Smoke Detection
KW - FMCW-Radar
KW - Low Visibility Environments
KW - Mobile Robotics
UR - http://www.scopus.com/inward/record.url?scp=85013130544&partnerID=8YFLogxK
U2 - 10.5220/0005960200300036
DO - 10.5220/0005960200300036
M3 - Paper
SP - 30
EP - 36
T2 - 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016
Y2 - 29 July 2016 through 31 July 2016
ER -