Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters

Timo Korthals, Marvin Barther, Thomas Schöpping, Stefan Herbrechtsmeier, Ulrich Rückert

Abstract

A huge number of techniques for detecting and mapping obstacles based on LIDAR and SONAR exist, though not taking approximative sensors with high levels of uncertainty into consideration. The proposed mapping method in this article is undertaken by detecting surfaces and approximating objects by distance using sensors with high localization ambiguity. Detection is based on an Inverse Particle Filter, which uses readings from single or multiple sensors as well as a robot’s motion. This contribution describes the extension of the Sequential Importance Resampling filter to detect objects based on an analytical sensor model and embedding into Occupancy Grid Maps. The approach has been applied to the autonomous mini robot AMiRo in a distributed way. There were promising results for its low-power, low-cost proximity sensors in various real life mapping scenarios, which outperform the standard Inverse Sensor Model approach.

References

  1. Benet, G., Blanes, F., Simó, J., and Pérez, P. (2002). Using infrared sensors for distance measurement in mobile robots. Robotics and Autonomous Systems, 40(4):255-266.
  2. Carlson, J. and Murphy, R. (2005). Use of DempsterShafer Conflict Metric to Detect Interpretation Inconsistency. Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005) , abs/1207.1.
  3. Carvalho, J. and Ventura, R. (2013). Comparative evaluation of occupancy grid mapping methods using sonar sensors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7887 LNCS:889-896.
  4. Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer, 22(6):46-57.
  5. Elfes, A. (1992). Dynamic control of robot perception using multi-property inference grids.
  6. Hähnel, D. (2004). Mapping with Mobile Robots. PhD thesis.
  7. Herbrechtsmeier, S., Rückert, U., and Sitte, J. (2012). AMiRo - Autonomous Mini Robot for research and education. Springer Berlin Heidelberg, Berlin, Heidelberg.
  8. Kleppe, A. L. and Skavhaug, A. (2013). Obstacle detection and mapping in low-cost, low-power multi-robot systems using an Inverted Particle Filter.
  9. Korthals, T., Krause, T., and Rückert, U. (2015). Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks.Machine Learning for Cyber Physical Systems, 1(1):6.
  10. Matthies, L. and Elfes, A. (1988). Integration of sonar and stereo range data using a grid-based representation. In Robotics and Automation, 1988. Proceedings., 1988 IEEE International Conference on, pages 727-733. IEEE.
  11. Moravec, H. and Elfes, a. (1985). High resolution maps from wide angle sonar. Proceedings. 1985 IEEE International Conference on Robotics and Automation, 2.
  12. Navarro, I. and Matía, F. (2013). An Introduction to Swarm Robotics. ISRN Robotics, 2013:1-10.
  13. Plascencia, A. C. and Bendtsen, J. D. (2009). Sensor Fusion Map Building-Based on Fuzzy Logic Using Sonar and SIFT Measurements. In Applications of Soft Computing, pages 13-22. Springer.
  14. Stachniss, C. (2009). Robotic Mapping and Exploration.
  15. Thrun, S. (2002). Robotic Mapping: A Survey. (February).
  16. Thrun, S. (2003). Learning occupancy grid maps with forward sensor models. Autonomous robots, 15(2):111- 127.
  17. Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics.
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Paper Citation


in Harvard Style

Korthals T., Barther M., Schöpping T., Herbrechtsmeier S. and Rückert U. (2016). Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters . In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-198-4, pages 192-200. DOI: 10.5220/0005960001920200


in Bibtex Style

@conference{icinco16,
author={Timo Korthals and Marvin Barther and Thomas Schöpping and Stefan Herbrechtsmeier and Ulrich Rückert},
title={Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2016},
pages={192-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005960001920200},
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 - Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters
SN - 978-989-758-198-4
AU - Korthals T.
AU - Barther M.
AU - Schöpping T.
AU - Herbrechtsmeier S.
AU - Rückert U.
PY - 2016
SP - 192
EP - 200
DO - 10.5220/0005960001920200