Neural Network-Based SLAM/GNSS Fusion Localization Algorithm for Agricultural Robots in Orchard GNSS-Degraded or Denied Environments

To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorithm. First, a tightly-coupled lidar inertial odometry via smoothing and mapping algorithm is employed to obtain the robot's observed pose in the SLAM coordinate system without prior maps. Subsequently, dual-antenna Real-Time Kinematic measurements are utilized to acquire positioning and orientation data. These data are then processed through Gauss-Kruger projection and coordinate transformation to derive the robot's observed pose in the GNSS coordinate system. Then, coordinate system alignment preprocessing is implemented to unify the coordinate system of multi-sensor observed poses, followed by outlier filtering and drift correction to optimize the SLAM poses. Finally, a neural network-based dynamic weight adjustment fusion localization algorithm is designed to integrate pose observations from two distinct coordinate systems, thereby enhancing the robot’s localization accuracy and stability in weak or GNSS-denied environments. Experimental results on the robotic platform demonstrate that, compared to the SLAM algorithm without pose optimization, the proposed SLAM/GNSS fusion localization algorithm reduced the whole process average position deviation by 37%. Compared to the fixed-weight fusion localization algorithm, the proposed SLAM/GNSS fusion localization algorithm achieved a 74% reduction in average position deviation during transitional segments with GNSS signal degradation or recovery. These results validate the superior positioning accuracy and stability of the proposed SLAM/GNSS fusion localization algorithm in weak or GNSS-denied environments. Orchard experimental results demonstrate that, at an average speed of 0.55m/s, the proposed SLAM/GNSS fusion localization algorithm achieves an overall average position deviation of 0.12m, with average position deviation of 0.06m in high GNSS signal quality zones, 0.11m in transitional sections under signal degradation or recovery, and 0.14m in fully GNSS-denied environments. These results validate the proposed SLAM/GNSS fusion localization algorithm maintains high localization accuracy and stability even under conditions of low and highly fluctuating GNSS signal quality, meeting the operational requirements of most agricultural robots.