The road condition in the field environment is complex, and the accurate localization of unmanned vehicle is critical to solve the SLAM (Simultaneous Localization and Mapping) problem. In this paper, GPS, IMU and odometer sensors are selected as hardware equipment, and the strategy of combining global positioning and local positioning is formulated. The sensor data fusion is utilized with the more adaptive unscented Kalman filter algorithm as the core. The vehicle experiment is carried out in the real field environment to ensure that the localization system can precisely output the coordinates of unmanned vehicles under the condition of high nonlinearity as well as meet the robustness and accuracy requirements of unmanned vehicle localization in the field environment.


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    Title :

    Three-dimensional positioning of unmanned vehicle in field based on unscented Kalman Filter


    Contributors:
    Li, Xiaolong (author) / Zhang, Chi (author) / Jin, Ma (author) / Xia, Kerui (author) / Cao, Xingyu (author) / Liu, Yiqun (author)

    Conference:

    4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022) ; 2022 ; Guangzhou,China


    Published in:

    Proc. SPIE ; 12257


    Publication date :

    2022-08-01





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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