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 -