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GNSS-based positioning and navigation has been widely used for agricultural robots in open unmanned farms. However, for the applications of semi-structured and semi-open agricultural scenarios, there may be temporary loss of GNSS received signals caused by occlusion of canopies in some areas, which will affect the positioning and navigation accuracy of robots and even harm crops or farmers. To solve this problem, a combined positioning method of GNSS and INS under the occlusion environment of agriculture was studied. The main work consisted of three parts: a mobile agricultural robot system was build up for the experiments of multi-sensor-based positioning and navigation, which consisted of hardware (track-layer mobile platform, GNSS receivers and INS, etc.) and software (ROS, remote control interface, etc.);an adaptive-coefficient Kalman filter based combined positioning algorithm was proposed. When the GNSS signal was unstable or denied, the new algorithm can switch to INS positioning adaptively based on Kalman filter, which carried out the optimal estimation for the robots’ location and gesture;experiments of the proposed combined positioning algorithm were conducted under practical scenes of agriculture, in which four different positioning methods (GNSS only, INS only, Kalman filter based combined positioning, and adaptive-coefficient Kalman filter based combined positioning) were compared to validate the effectiveness of the algorithm. Field experiments showed that in the process of combined positioning, compared with GNSS positioning, INS positioning and conventional Kalman filter fusion positioning, the positioning accuracy of adaptive-coefficient Kalman filter in the 30m×6m high shaded area of 100m×20m experimental area was improved by 62.1%,48.5% and 47.7%, respectively.