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Bush spherical center detection algorithm based on depth camera 3D point cloud
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Automated pruning is an inevitable trend in the improvement of modern gardens. In order to provide necessary information for automatic garden robots and satisfy the requirement of target detection and positioning during pruning, this paper proposed a bush spherical center detection algorithm based on a 3D depth camera point cloud. Firstly, the depth camera collected the bush image, and the results were aligned to the depth image to obtain the 3D point cloud of bush. Then the ROI was extracted by preprocessing, and the 3D point clouds of bush was obtained after filtering and coordinate transformation. Finally, the spherical center coordinates of the bush were extracted by the minimum bounding box method. Four groups of tests on the bush spherical coordinates detection were carried out outdoors. The maximum location error and the minimum location error of the spherical bush center were 10.23mm and 8.65 mm, respectively, and the average location error was 9.51mm. The bush spherical center detection algorithm based on depth camera 3D point clouds proposed in this paper provides a technical reference for the information acquisition of automatic pruning robot.
Title: Bush spherical center detection algorithm based on depth camera 3D point cloud
Description:
Automated pruning is an inevitable trend in the improvement of modern gardens.
In order to provide necessary information for automatic garden robots and satisfy the requirement of target detection and positioning during pruning, this paper proposed a bush spherical center detection algorithm based on a 3D depth camera point cloud.
Firstly, the depth camera collected the bush image, and the results were aligned to the depth image to obtain the 3D point cloud of bush.
Then the ROI was extracted by preprocessing, and the 3D point clouds of bush was obtained after filtering and coordinate transformation.
Finally, the spherical center coordinates of the bush were extracted by the minimum bounding box method.
Four groups of tests on the bush spherical coordinates detection were carried out outdoors.
The maximum location error and the minimum location error of the spherical bush center were 10.
23mm and 8.
65 mm, respectively, and the average location error was 9.
51mm.
The bush spherical center detection algorithm based on depth camera 3D point clouds proposed in this paper provides a technical reference for the information acquisition of automatic pruning robot.
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