Details
| Original language | English |
|---|---|
| Title of host publication | Informatics in Control, Automation and Robotics |
| Editors | Dimitri Peaucelle, Kurosh Madani, Oleg Gusikhin |
| Pages | 175-189 |
| Number of pages | 15 |
| Publication status | Published - 2018 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 430 |
| ISSN (Print) | 1876-1100 |
| ISSN (electronic) | 1876-1119 |
Abstract
LiDAR sensors are very popular for mapping and localisation with mobile robots, yet they cannot handle harsh environments, containing smoke, fog, dust, etc. On the other hand, radar sensors can overcome these situations, but they are not able to represent an environment in the same quality as a LiDAR due to their limited range and angular resolution. In the following article, we present further results regarding SLAM involving the mechanical pivoting radar (MPR), which is a 2D high bandwidth radar scanner that was introduced in Fritsche et al. (Radar and LiDAR sensor fusion in low visibility environments, 2016, [8]). We present two strategies for fusing MPR and LiDAR data to achieve SLAM in an environment with low visibility. The first approach is based on features and requires the presence of landmarks, which can be extracted with LiDAR and MPR. The second SLAM approach is based on scan registration and requires a scan fusion between the two sensors. In the end, we show our experiments, involving real fog, in order to demonstrate, how our approaches make SLAM possible in harsh environments.
Keywords
- LiDAR, Mobile robots, Radar, SLAM, Sensor fusion
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
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Informatics in Control, Automation and Robotics. ed. / Dimitri Peaucelle; Kurosh Madani; Oleg Gusikhin. 2018. p. 175-189 (Lecture Notes in Electrical Engineering; Vol. 430).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Fusing LiDAR and Radar Data to Perform SLAM in Harsh Environments.
AU - Fritsche, Paul
AU - Kueppers, Simon
AU - Briese, Gunnar
AU - Wagner, Bernardo
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions. Funding Information: Acknowledgements. This work has partly been supported within H2020-ICT by the European Commission under grant agreement number 645101 (SmokeBot).
PY - 2018
Y1 - 2018
N2 - LiDAR sensors are very popular for mapping and localisation with mobile robots, yet they cannot handle harsh environments, containing smoke, fog, dust, etc. On the other hand, radar sensors can overcome these situations, but they are not able to represent an environment in the same quality as a LiDAR due to their limited range and angular resolution. In the following article, we present further results regarding SLAM involving the mechanical pivoting radar (MPR), which is a 2D high bandwidth radar scanner that was introduced in Fritsche et al. (Radar and LiDAR sensor fusion in low visibility environments, 2016, [8]). We present two strategies for fusing MPR and LiDAR data to achieve SLAM in an environment with low visibility. The first approach is based on features and requires the presence of landmarks, which can be extracted with LiDAR and MPR. The second SLAM approach is based on scan registration and requires a scan fusion between the two sensors. In the end, we show our experiments, involving real fog, in order to demonstrate, how our approaches make SLAM possible in harsh environments.
AB - LiDAR sensors are very popular for mapping and localisation with mobile robots, yet they cannot handle harsh environments, containing smoke, fog, dust, etc. On the other hand, radar sensors can overcome these situations, but they are not able to represent an environment in the same quality as a LiDAR due to their limited range and angular resolution. In the following article, we present further results regarding SLAM involving the mechanical pivoting radar (MPR), which is a 2D high bandwidth radar scanner that was introduced in Fritsche et al. (Radar and LiDAR sensor fusion in low visibility environments, 2016, [8]). We present two strategies for fusing MPR and LiDAR data to achieve SLAM in an environment with low visibility. The first approach is based on features and requires the presence of landmarks, which can be extracted with LiDAR and MPR. The second SLAM approach is based on scan registration and requires a scan fusion between the two sensors. In the end, we show our experiments, involving real fog, in order to demonstrate, how our approaches make SLAM possible in harsh environments.
KW - LiDAR
KW - Mobile robots
KW - Radar
KW - SLAM
KW - Sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85034229230&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-55011-4_9
DO - 10.1007/978-3-319-55011-4_9
M3 - Contribution to book/anthology
SN - 9783319550107
T3 - Lecture Notes in Electrical Engineering
SP - 175
EP - 189
BT - Informatics in Control, Automation and Robotics
A2 - Peaucelle, Dimitri
A2 - Madani, Kurosh
A2 - Gusikhin, Oleg
ER -