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)


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.


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



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.


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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

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