Preliminary Development of a Line Feature-Based Object Recognition System for Textureless Indoor Objects

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This paper presents preliminary results of a textureless object recognition system for an indoor mobile robot. Our approach relies on 1) segmented linear features, and 2) pairwise geometric relationships between features. This approach is motivated by the need for recognition strategies that can handle many of indoor objects that have no little or not textural information on their surfaces, but have strong geometrical consistency within the same object class. Our matching method consists of two steps. First, we find correspondence candidates between linear fragments. Second, a spectral matching algorithm is used to find the subset of correspondences which is the most consistent. Both matching methods are learnt by using logistic classifiers. We evaluated the developed recognition system with our own database, which is composed of eight indoor object classes. We also compared the performance of our line feature based recognition approach with a SIFT feature based method. Experimentally, it turned out that the line features are superior in our problem setup - the detection of textureless objects.