Abstract
One or two RGB-D images cannot provide enough information to detect cut-off points in pruning systems. 3D semantic point clouds constructed from many RGB-D images represent real tomato plants and help the system find the cut-off points correctly. We proposed a method to create 3D semantic point clouds based on ORB-SLAM3, ICP (iterative closet point) algorithm, and semantic segmentation neural network. RGB-D images are converted to semantic images by the semantic segmentation neural network. Each pair of camera poses which is estimated by ORB-SLAM3, and an RGB-D semantic image is used to create a 3D point cloud. The ICP method is applied to stick and refine these point clouds to construct a full 3D semantic point cloud.
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