The 3D object recognition with environmental adaptability based on VFH descriptor and region growing segmentation

3D object recognition is a basic research in the machine vision field. Microsoft KINECT V2 is utilized to collect external environmental information. The point cloud file is obtained after processing the collected information. In order to filter the point cloud and obtain point cloud model of a single object in the environment after region growing segmentation, the point cloud is applied to point cloud library. Then, the VFH descriptor of the point cloud model is calculated. After inputting point cloud model of the trained target, the point cloud model with the minimum CHI square distance between the VFH descriptor of the target and VFH descriptor of the point cloud model can be found. The 3D object corresponding to the found model is the identified object. For the 3D object recognition in an unfamiliar environment, the algorithm of 3D object recognition with environmental adaptability is proposed. After the 3D object recognition system built, the physical verification is conducted about the proposed algorithm. Giving the target model, the system successfully identifies the 3D object in the unfamiliar environment, that demonstrates the correctness of the algorithm.