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Radar and LiDAR sensorfusion in low visibility environments

: Fritsche, P.; Kueppers, S.; Briese, G.; Wagner, B.


Gusikhin, O. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
ICINCO 2016, 13th International Conference on Informatics in Control, Automation and Robotics. Proceedings. Vol.2 : Lisbon, Portugal, July 29-31, 2016
SciTePress, 2016
ISBN: 978-989-758-198-4
International Conference on Informatics in Control, Automation and Robotics (ICINCO) <13, 2016, Lisbon>
Fraunhofer FHR ()

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 discus sing its meaning and possible application.