# GNSS and LiDAR Integrated Navigation Method in Orchards with Intermitt GNSS Dropout
## 1 Introduction  
At present, China is the world's largest producer and consumer of forest fruit [1], with the State Bureau of Statistics reporting that in 2021, orchards occupied approximately 12,962 thousand hectares, marking a 2.5% increase, and the annual fruit production in China was 296.11 million tons. However, compared with farmland production, the mechanization rate in orchards is low, and complex tasks such as picking, transportation, weeding, and pruning are common. The issue of seasonal employment makes it difficult to hire expensive workers, and the rising labor cost has become an important factor restricting the improvement of the forest and fruit industry. Therefore, it is vital to enhance the mechanization level of orchard industry and improve the efficiency of orchard operations [2, 3, 4]. In view of the unique advantages of intelligent operating equipment in complex agricultural production, autonomous navigation technology in orchards has become more and more widely used in recent years, and the orchard autonomous navigation technology has become an indispensable core technology in the intelligent operation equipment. The autonomous navigation technology of operational equipment involves its own precise positioning, path planning, path-tracking control, and other functions, which is an important prerequisite for realizing intelligent and efficient operation of orchard machinery. The real-time kinematic global satellite navigation system (RTK-GNSS) is currently one of the most widely utilized navigation technologies. It achieves centimeter-level accuracyin satellite positioning by processing carrier phase measurements as a ranging source in open outdoor environments [5]. It provides real-time, absolute positioning with broad coverage, all-weather capabilities, and high precision [6; 7; 8]. However, the tree canopy and bird-proof nets block the satellite signals [5], making it difficult to ensure continuous high-precision autonomous movement. Therefore, it is necessary to integrate information from other sensors to overcome this drawback. Recently, some robot navigation frameworks have been proposed based on combinations of different types of sensor information, including GNSS-INS [9; 10], GNSS-Visual [11; 12; 13], and GNSS-LiDAR [14; 15; 16]. Among them, inertial navigation is widely used in the case of transient loss of satellite signals due to its advantages of isolation from the environment and high operational accuracy in a short period of time. [PERSON] et al. [17] designed an integrated navigation system based on real-time dynamic kinematics BeiDou satellite navigation system (RTK-BDS) and INS and conducted navigation experiments using an agricultural machine to keep the position error within 3 cm on an open road. [PERSON] et al. [18] fused the GNSS with Kalman-filter-processed inertial measurement unit (IMU) information for position estimation between GNSS position updates to improve the positioning accuracy. [PERSON] et al. [19] greatly improve the accuracy and robustness of high-precision localization in forests by utilizing GNSS/INS to obtain high-precision attitude and velocity information via fusion of the two sensors. Due to the accuracy limitation of inertial devices, INS has a drift error accumulated over time [20]. This drift makes INS unsuitable for orchard environments where satellite signals are blocked for extended periods, and the terrain is uneven and overgrown with weeds. The positioning of a visual sensor is obtained by transforming the pixel position, pixel-camera distance, camera coordinate system, and robot coordinate system. [PERSON] et al. [21] used a GNSS/INS-based navigation system and machine-vision-based obstacle monitoring to achieve normal operation and the obstacle avoidance function of the robot without a GNSS signal for five minutes. [PERSON] et al. [22] proposed a GNSS navigation method enhanced by vision, which uses a Kalman filter to integrate binocular vision with GNSS-provided positioning.