Improved Real-Time Semantic Segmentation Network Model for Crop Vision Navigation Line Detection

Research on orchard navigation method based on fusion of 3D SLAM and point cloud positioning

Accurate navigation is crucial in the construction of intelligent orchards, and the need for vehicle navigation accuracy becomes even more important as production is refined. However, traditional navigation methods based on global navigation satellite system (GNSS) and 2D light detection and ranging (LiDAR) can be unreliable in complex scenarios with little sensory information due to tree canopy occlusion. To solve these issues, this paper proposes a 3D LiDAR-based navigation method for trellis orchards. With the use of 3D LiDAR with a 3D simultaneous localization and mapping (SLAM) algorithm, orchard point cloud information is collected and filtered using the Point Cloud Library (PCL) to extract trellis point clouds as matching targets. In terms of positioning, the real-time position is determined through a reliable method of fusing multiple sensors for positioning, which involves transforming the real-time kinematics (RTK) information into the initial position and doing a normal distribution transformation between the current frame point cloud and the scaffold reference point cloud to match the point cloud position. For path planning, the required vector map is manually planned in the orchard point cloud to specify the path of the roadway, and finally, navigation is achieved through pure path tracking. Field tests have shown that the accuracy of the normal distributions transform (NDT) SLAM method can reach 5 cm in each rank with a coefficient of variation that is less than 2%. Additionally, the navigation system has a high positioning heading accuracy with a deviation within 1° and a standard deviation of less than 0.6° when moving along the path point cloud at a speed of 1.0 m/s in a Y-trellis pear orchard. The lateral positioning deviation was also controlled within 5 cm with a standard deviation of less than 2 cm. This navigation system has a high level of accuracy and can be customized to specific tasks, making it widely applicable in trellis orchards with autonomous navigation pesticide sprayers.

High precision visual localization method of UAV based on feature matching

In this paper, the precision hovering problem of UAV operation is studied. Aiming at the diversity and complexity of the UAV operating environment, a high-precision visual positioning and orientation method based on image feature matching was proposed. The image feature matching based on the improved AKAZE algorithm is realized, and the optimal matching point pair screening method based on the fusion of Hamming distance and matching line angle is innovatively proposed, which greatly improves the robustness of the algorithm without affecting the performance of the algorithm. The real-time image is matched with the benchmark image for image feature matching. By reducing the deviation of image feature, the pose state correction of UAV hovering is achieved, and the precision hovering of the UAV is realized. Both simulation and real UAV tests verify the effectiveness of the proposed UAV high-precision visual positioning and orientation method.