Depth Camera and UWB Positioning Data Fusion Algorithm for Indoor Picking Drones

Indoor growing is a trend in agriculture in the future. Using drones to replace manual monitoring and picking of fruits can save costs and free up manpower. Achieving precise picking by drones requires strong indoor positioning capabilities, but in the complex environment of indoor orchards, using a single sensor may produce data fluctuations. This project proposes a method to fuse depth camera data and UWB data to solve the problem of insufficient positioning performance of a single sensor: independently obtain the positioning data of the depth camera and UWB, use the Kalman filter to process the data noise, and calculate the data fluctuations degree, calculate the sensor data confidence, compare and obtain the sensor data with high confidence as the preliminary data for fusion, and finally perform Kalman filter processing on the preliminary fusion data again to obtain the final data. This fusion method achieves high-precision indoor positioning of UAVs. Through simulation experiments, compared with using depth cameras or UWB data independently, the data fusion solution improves the maximum error performance by approximately 18.05% and the minimum mean square error performance. Increased by approximately 4.76%.