Authors:
Paul Fritsche
1
;
Simon Kueppers
2
;
Gunnar Briese
2
and
Bernardo Wagner
1
Affiliations:
1
Institute of Systems Engineering - Real Time Systems Group and Leibniz Universität Hannover, Germany
;
2
Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Germany
Keyword(s):
Mobile Robotics, Low Visibility Environments, FMCW-Radar, LiDAR, Sensorfusion, Smoke Detection.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
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 meani
ng and possible application.
(More)