3D Object Recognition Based on Local and Global Features Using Point Cloud Library

3D object recognition from point clouds is considered as a field of research that is growing fast. Based on the types of features used to represent an object, 3D object recognition approaches can be classified into two broad categories—local and global feature-based techniques. Local feature-based techniques are more robust to clutter and partial occlusions that are frequently present in a real-world scene. Whereas, global feature-based techniques are suitable for model retrieval and 3D shape classification especially with the weak geometric structure. Most systems for 3D object recognition use either local or global feature-based techniques. This is because of the difficulty of integrating a set of local features with a single global feature vector in an appropriate manner. In this paper, a 3D object recognition system based on local and global features of the objects using Point Cloud Library (PCL) is proposed. The proposed system uses a hybrid technique based on Viewpoint Feature Histogram (VFH) method and Fast Point Feature Histogram (FPFH) method. VFH method is used as a global descriptor to recognize the object. Whereas, FPFH method is used as a local descriptor to estimate the position of the object in the real-world scene. The performance of the proposed system is evaluated by calculating the accuracy of the recognition process. The experimental results reveal that this system performs well on the tested objects as compared to some state of the art techniques.

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