A Novel Mapping and Navigation Framework for Robot Autonomy in Orchards

Target detection is a basic task to divide the object types in the orchard point cloud global map, which is used to count the overall situation of the orchard. And provide necessary information for unmanned navigation planning of agricultural vehicles. In order to divide the fruit trees and the ground in the point cloud global map of the standardized orchard, and provide the orchard overall information for the path planning of autonomous vehicles in the natural orchard environment. A fruit tree detection method based on the Yolo-V7 network is proposed, which can effectively detect fruit tree targets from multi-sensor fused radar point cloud, reduce the 3D point cloud information of the point cloud map to 2D for the fruit tree point cloud in the Yolo-V7 network detection map, and project the prediction results into the point cloud map. Generally, the target detection network based on PointNet has the problem of low speed and large computational load. The method proposed in this paper is fast and low computational load and is suitable for deployment in mobile robots. From the experimental results, the recall rate and accuracy rate of the proposed method in orchard fruit tree detection are 0.4 and 0.696 respectively, and its weight and reasoning time are 7.4 M and 28 ms respectively. The experimental results show that this method can achieve the robustness and efficiency of real-time detection of orchard fruit trees.