Advertisement

Customizable Inverse Sensor Model for Bayesian and Dempster-Shafer Occupancy Grid Frameworks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)

Abstract

Occupancy grid mapping is an important component in a road scene understanding for autonomous driving. It can encapsulate data from heterogeneous sensor sources like radars, LiDARs, cameras and ultrasonics. At the core of occupancy grid (OG) generation, there is usually an inverse sensor model (ISM), which infers the occupancy representation from the sensor readings. Traditional ISMs are characterized by a very rigid structure, suited only for one sensor type, and specific occupancy grid representation. This paper proposes a novel ISM framework, which offers a separation between free and occupied space, supporting both Bayesian and Dempster-Shafer OG representations. The framework is especially useful when dealing with multiple different sensors where custom or preselected probability distribution can be applied. The presented ISM architecture is modular and flexible, which is described in an illustrative example of application customized for different detection sources.

Keywords

Inverse sensor modeling Occupancy grid Environment mapping methods Bayesian inference Dempster-shafer evidence theory Sensor models 

Notes

Acknowledgment

Research was funded by Polish Ministry of Science and Higher Education (MNiSW) Project No. 0014/DW/2018/02 and carried out in cooperation of Aptiv Services Poland S.A. – Technical Center Kraków and AGH University of Science and Technology – Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering.

Special thanks to Aptiv coworkers: Krzysztof Kogut, Maciej Różewicz and Dariusz Cieślar for exceptional ideas and support.

References

  1. 1.
    Andriamahefa, T.R.: Integer occupancy grids: a probabilistic multi-sensor fusion framework for embedded perception. Ph.D. thesis, Université Grenoble Alpes (2017)Google Scholar
  2. 2.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)CrossRefGoogle Scholar
  3. 3.
    Dietmayer, K.C., Reuter, S., Nuss, D.: Representation of fused environment data. In: Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort, pp. 1–30 (2014)Google Scholar
  4. 4.
    Elfes, A., Matthies, L.: Sensor integration for robot navigation: combining sonar and stereo range data in a grid-based representataion. In: 26th IEEE Conference on Decision and Control 1987, vol. 26, pp. 1802–1807. IEEE (1987)Google Scholar
  5. 5.
    Foroughi, M., Iurgel, U., Ioffe, A., Doerr, W.: Free space grid for automotive radar sensors. In: FAST-zero 2015: 3rd International Symposium on Future Active Safety Technology Toward zero traffic accidents (2015)Google Scholar
  6. 6.
    Homm, F., Kaempchen, N., Ota, J., Burschka, D.: Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 1006–1013. IEEE (2010)Google Scholar
  7. 7.
    Joubert, D.: Adaptive occupancy grid mapping with measurement and pose uncertainty. Ph.D. thesis. Stellenbosch University, Stellenbosch (2012)Google Scholar
  8. 8.
    Konolige, K.: Improved occupancy grids for map building. Auton. Robots 4(4), 351–367 (1997)CrossRefGoogle Scholar
  9. 9.
    Markiewicz, P., Kogut, K., Różewicz, M., Skruch, P., Starosolski, R.: Occupancy grid fusion prototyping using automotive virtual validation environment. In: Proceedings of the 6th International Conference on Control, Mechatronics and Automation, pp. 81–85. ACM (2018)Google Scholar
  10. 10.
    Milstein, A.: Occupancy grid maps for localization and mapping. In: Motion Planning, IntechOpen (2008)Google Scholar
  11. 11.
    Moravec, H.P.: Sensor fusion in certainty grids for mobile robots. AI Mag. 9(2), 61 (1988)Google Scholar
  12. 12.
    Pathak, K., Birk, A., Poppinga, J., Schwertfeger, S.: 3D forward sensor modeling and application to occupancy grid based sensor fusion. In: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, pp. 2059–2064. IEEE (2007)Google Scholar
  13. 13.
    Stachniss, C.: Exploration and mapping with mobile robots. Ph. D. thesis, University of Freiburg (2006)Google Scholar
  14. 14.
    Stepan, P., Kulich, M., Preucil, L.: Robust data fusion with occupancy grid. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 35(1), 106–115 (2005)CrossRefGoogle Scholar
  15. 15.
    Thrun, S.: Learning occupancy grid maps with forward sensor models. Auton. Robots 15(2), 111–127 (2003)CrossRefGoogle Scholar
  16. 16.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT press, Cambridge (2005)zbMATHGoogle Scholar
  17. 17.
    Valente, M., Joly, C., de La Fortelle, A.: Grid matching localization on evidential slam. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1477–1483. IEEE (2018)Google Scholar
  18. 18.
    Weiss, T., Schiele, B., Dietmayer, K.: Robust driving path detection in urban and highway scenarios using a laser scanner and online occupancy grids. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 184–189. IEEE (2007)Google Scholar
  19. 19.
    Weston, R., Cen, S., Newman, P., Posner, I.: Probably unknown: deep inverse sensor modelling in radar (2018). arXiv preprint arXiv:1810.08151
  20. 20.
    Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: A probabilistic, flexible, and compact 3D map representation for robotic systems. In: Proceedings of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation, vol. 2 (2010)Google Scholar
  21. 21.
    Yguel, M., Aycard, O., Laugier, C.: Efficient GPU-based construction of occupancy grids using several laser range-finders. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 105–110. IEEE (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and RoboticsAGH University of Science and TechnologyKrakówPoland
  2. 2.Aptiv Services Poland S.A.KrakówPoland

Personalised recommendations