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

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.