GENERIC OBJECT RECOGNITION BASED ON FEATURE FUSION IN ROBOT PERCEPTION

Xinde Li, Chaomin Luo, Jean Dezert, and Yingzi Tan

Keywords

Generic object recognition, Point cloud, SIFT, Feature fusion, SVM, belief functions

Abstract

A new generic object recognition (GOR) method for robot perception is proposed in this paper, based on multi-feature fusion of two-dimensional (2D) and 3D scale invariant feature transform descriptors drawn from 2D images and 3D point clouds. The trained support vector machine is utilized to construct multi-category classifiers that recognize the objects. According to our results, this new GOR approach achieves higher recognition rates than classical methods tested, even when one has large intra-class variations, or high inter-class similarities of the objects. Simulation results demonstrate the effectiveness and efficiency of the proposed GOR approach.

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