Three-dimensional mapping utilizing stereo vision and Bayesian inference

Tatsunori Kou, Kenji Suzuki, Shuji Hashimoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    In this study we propose a method for creating 3D map of real world environment by using 3D occupancy grids. The map is created by characterizing each grid associated with a certain area in the real world environment by utilizing multiple measurements using stereo vision and Bayesian inference. The proposed method can absorb the measurement uncertainties caused in the stereo matching process and in the system's calibrations. The preliminary experiments show that the proposed algorithm is able to robustly generate environment maps. The algorithm is also suitable to be implemented as a vision system for autonomous mobile robots.

    Original languageEnglish
    Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
    EditorsS. Kaneko, H. Cho, G.K. Knopf, R. Tutsch
    Pages111-118
    Number of pages8
    Volume5603
    DOIs
    Publication statusPublished - 2004
    EventMachine Vision and its Optomechatronic Applications - Philadelphia, PA, United States
    Duration: 2004 Oct 262004 Oct 28

    Other

    OtherMachine Vision and its Optomechatronic Applications
    CountryUnited States
    CityPhiladelphia, PA
    Period04/10/2604/10/28

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    Keywords

    • Bayesian inference
    • Mapping
    • Mobile robots
    • Occupancy grids
    • Stereo vision

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Condensed Matter Physics

    Cite this

    Kou, T., Suzuki, K., & Hashimoto, S. (2004). Three-dimensional mapping utilizing stereo vision and Bayesian inference. In S. Kaneko, H. Cho, G. K. Knopf, & R. Tutsch (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 5603, pp. 111-118). [15] https://doi.org/10.1117/12.580585