In recent years, the positioning technology for unmanned aerial vehicle (UAV) has been developed rapidly. However, there are still challenges in terms of improving the computational efficiency, accuracy, and robustness of the algorithm for applications in fast flight with UAV. In this paper, we present a novel positioning system of UAV based on IMA-GPS (inertial measurement unit array -global positioning system) three-layer data fusion, which effectively solves the positioning problem of UAV. The first layer is the distributed filtering layer, the unscented Kalman filter serves as the local filter to process the measurements generated by each IMU in the IMA. The second layer is the IMA compensation fusion layer, the data of GPS is introduced to compensate for the deviation of the navigation results calculated by each IMU. The third layer is the GPS-IMA joint optimization layer, where the outputs of the IMA and GPS are fused to obtain the attitude, speed, and position. Simulation results show that the proposed method can realize an integrated navigation system which has low cost, high accuracy, and high computational speed, so that the performance of the UAV positioning system will be improved greatly.INDEX TERMS IMA, unscented Kalman filter, multi-sensor data fusion, UAV positioning system.

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Abstract. Work pieces' location based on machine visual in robot unstacking job has been researched. Adaptive threshold segmentation and modified Hough Transform are used to extract the target region needs further processing. A new method is proposed to choose the appropriate threshold to binary different images with different brightness. Watershed algorithm and Morphology algorithm are used to detect and connect the edges of work pieces. With the distance sensor and camera calibration to complete the location of pallet and work pieces, guiding robot to finish unstacking job successfully.