106. The 3D object recognition with environmental adaptability based on VFH descriptor and region growing segmentation
Zhuang Peng1, Jinbao Chen2, Dong Han3, Meng Chen4
1AVIC Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu, China
2, 3Nanjing University of Aeronautics and Astronautics, Nanjing, China
4Shanghai Institute of Aerospace Systems Engineering, Shanghai, China
E-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Received 12 May 2015; received in revised form 4 October 2016; accepted 17 October 2016
Abstract. 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.
Keywords: VFH descriptor, region growing segmentation, CHI square distance, environmental adaptability, 3D object recognition.
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Cite this article
Peng Zhuang, Chen Jinbao, Han Dong, Chen Meng The 3D object recognition with environmental adaptability based on VFH descriptor and region growing segmentation. Journal of Measurements in Engineering, Vol. 4, Issue 4, 2016, p. 195‑200.
Journal of Measurements in Engineering. December 2016, Volume 4, Issue 4
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