# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 1 Introduction  
The processes of orchard planting, management, and fruit harvesting in China are confronted with the problems of a low degree of mechanization, high labor intensity, and high cost [1]. Pest control is one of the most important links in the fruit production, performed by spraying pesticides 8-15 times a year, accounting for around 30% of the whole workload in the orchard management [2, 3]. Recently, some automatic controlled orchard spraying robots have been developed to reduce the labor intensity and improve efficiency. The navigation system, including positioning and calculating paths, is an important part of the control system of orchard spraying robots. With the development of navigation and positioning systems [4], technologies such as laser navigation [5; 6; 7], visual navigation [8; 9; 10] and Global Navigation Satellite System (GNSS) navigation [11; 12] have become research hotspots. [PERSON] et al. [13] used the Hough transform algorithm to recognize the tree rows using a laser scanner as a navigation sensor, and the results performed on a tractor show that the lateral error was 0.11 m and the heading error was 1.5\({}^{\circ}\). [PERSON] et al. [14] developed a navigation system based on a laser rangefinder for a trailer whose moving direction is controlled by a sliding hitch bar, and its RMS difference was 0.275 m. [PERSON] et al. [15] proposed an algorithm for the lidar data obtained in the orchard, where a multi-support-vector proportion weighting method is developed to identify the ridge line of vineyards with the average angular deviation of 0.72\({}^{\circ}\). The visual navigation technology, featured with wide range of detection and good cost-efficiency, is one of the mainstream navigation methods [16]. [PERSON] et al. [17] proposed a visual navigation method based on U-Net for path recognition, the average pixel error was 9.5 pixels, but this method is only applicable to orchards with visually explicit crop and road edges. [PERSON] et al. [18] built a navigation system utilizing the film on the single row ridge as the navigation feature, extracted the H channel in HSV colour space, combined with the Otsu algorithm to extract the row ridge, and fitted the navigation line with the least square method. [PERSON] et al. [19] proposed a method to extract citrus trunks based on YOLO V3 convolutional neural network, where the intersections of the trunks and ground were fitted into a navigation line by the least square method. [PERSON] et al. [20] proposed a navigation mark generation method to replace the root point of the fruit tree, where the base point coordinates were extracted from the tree rows based on the cubic spline interpolation method, and the navigation line was generated according to the least square method. [PERSON] et al. [21] fitted the navigation line according to the boundary line between the canopy and the sky, the root mean square error of the horizontal deviation was 2.13 cm when testing in a peach orchard, but this method is only suitable for those kinds of orchards having tree crowns all year round. Branches overhang interferes with the extraction of the boundary line, resulting in a reduction in navigation accuracy. With the development of satellite positioning technology, the error of carrier-phase differential satellite positioning is limited to a few centimeters, on which a lot of agricultural navigation systems were built. [PERSON] et al. [22] developed a navigation controller for tractor linear tracking based on a PID controller and Dongfanghong X-804 tractor, using RTK-DGPS positioning technology and combining tractor motion with a hydraulic steering control model. [PERSON] et al. [23] developed a navigation controller for rice transplanting based on RTK-GPS and fiber optic gyroscope sensors to measure position and direction, using actuators to control steering, engine throttle, clutch, brake, etc, where the maximum deviation from the desired path is less than 12 cm. In order to adapt to the orchard, [PERSON] et al.