2results about How to "Improve algorithm efficiency" patented technology

The present application belongs to the technical field of ground laser radar forestry data processing, and discloses a broad-leaved forest single tree segmentation method and system based on branch information guidance, taking ground-based laser radar broad-leaved forest point cloud as a processing object, using RANSAC cylindrical fitting to combine the growth characteristics of the tree trunk to detect the tree trunk; starting from the top of the tree trunk in the low vegetation area, the tree branches are extracted by segmenting and growing the branches and combining the thickness changes of the branch segments; starting from the end of the branch, the tree crown leaf point cloud is segmented by layer-by-layer growth. The present application has stronger trunk detection capability under the conditions of lush low vegetation and complex terrain; on the other hand, the present application can accurately segment the tree crown when the large and small crowns are intertwined and the multiple crowns are closely surrounded. The algorithm is efficient, simple and easy to use, and has great practical significance for improving the semantic understanding ability of forest scenes, assisting forest resource investigation, vegetation ecological research, and satellite remote sensing product calibration and verification.

This invention relates to the field of robot skill learning technology, specifically to an efficient robot skill learning method, system, device, program product, and medium. The method includes: obtaining the robot's task policy equation in Cartesian space; obtaining the expected trajectory at each time step of the robot's task policy; constructing the cost function of the robot's task policy; and obtaining the robot's final expected trajectory. This invention utilizes interactive reinforcement learning to integrate human and machine intelligence, improving algorithm efficiency and overcoming the limitations of existing reinforcement learning methods in robot operation applications. It enables real-world robot skill learning, allowing robots to be applied in a wider range of scenarios.