Radar and LiDAR Sensorfusion in Low Visibility Environments

Paul Fritsche, Simon Kueppers, Gunnar Briese, Bernardo Wagner

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.

References

  1. Adams, M. and Jose, E. (2012). Robotic navigation and mapping with radar. Artech House.
  2. Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J. (2000). Lof: Identifying density-based local outliers. ACM SIGMOD International Conference on Management of Data, 29(2):93-104.
  3. Brooker, G., Scheding, S., Bishop, M., and Hennessy, R. (2005). Development and application of millimeter wave radar sensors for underground mining. IEEE Sensors Journal, 5(6):12701280.
  4. Clark, S. and Dissanayake, G. (1999). Simultaneous localisation and map building using milimetre wave radar to extract natural features. Internation Conference on Robotics and Automation.
  5. Clark, S. and Whyte, H. D. (1998). The design of a high performance mmw radar system for autonomous land vehicle navigation. In Field and Service Robotics.
  6. Detlefsen, J., Rozmann, M., and Lange, M. (1993). 94 hgz 3-d imaging radar sensor for industrial environments. EARSeL ADVANCEA IN REMOTE SENSING.
  7. Fritsche, P. and Wagner, B. (2015). Comparison of two radar-based scanning-techniques for the use in robotic mapping. In Informatics in Control, Automation and Robotics (ICINCO), 2015 12th International Conference on, volume 01, pages 365-372.
  8. Grisetti, G. (2005). Improving grid-based slam with raoblackwellized particle filters by adaptive proposals and selective resampling.
  9. Marck, J. W., Mohamoud, A., van Heijster, R., et al. (2013). Indoor radar slam a radar application for vision and gps denied environments. In Radar Conference (EuRAD), 2013 European, pages 471-474. IEEE.
  10. Salman, R., Willms, I., Sakamoto, T., Sato, T., and Yarovoy, A. (2013). Environmental imaging with a mobile uwb security robot for indoor localisation and positioning applications. In Microwave Conference (EuMC), 2013 European, pages 1643-1646.
  11. Vivet, D., Checchin, P., and Chapuis, R. (2013). Localization and mapping using only a rotating fmcw radar sensor. Sensors.
  12. Willeke, K., Baron, P., and Martonen, T. (1993). Aerosol Measurement: Principles, Techniques and Applications, volume 6. [New York, NY]: Mary Ann Liebert, Inc., c1988-2007.
  13. Yamauchi, B. (2010). Fusing ultra-wideband radar and lidar for small ugv navigation in all-weather conditions. Proc. SPIE 7692, Unmanned Systems Technology XII.
Download


Paper Citation


in Harvard Style

Fritsche P., Kueppers S., Briese G. and Wagner B. (2016). Radar and LiDAR Sensorfusion in Low Visibility Environments . In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-198-4, pages 30-36. DOI: 10.5220/0005960200300036


in Bibtex Style

@conference{icinco16,
author={Paul Fritsche and Simon Kueppers and Gunnar Briese and Bernardo Wagner},
title={Radar and LiDAR Sensorfusion in Low Visibility Environments},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2016},
pages={30-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005960200300036},
isbn={978-989-758-198-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Radar and LiDAR Sensorfusion in Low Visibility Environments
SN - 978-989-758-198-4
AU - Fritsche P.
AU - Kueppers S.
AU - Briese G.
AU - Wagner B.
PY - 2016
SP - 30
EP - 36
DO - 10.5220/0005960200300036