This paper presents a 3D object recognition method and its implementation on a Robotic Navigation Aid (RNA) to allow real-time detection of indoor structural objects for the navigation of a blind person. The method segments a point cloud into numerous planar patches and extracts their Inter-Plane Relationships (IPRs). Based on the existing IPRs of the object models, the method defines 6 High Level Features (HLFs) and determines the HLFs for each patch. A Gaussian-Mixture-Model-based plane classifier is then devised to classify each planar patch into one belonging to a particular object model. Finally, a recursive plane clustering procedure is used to cluster the classified planes into the model objects. As the proposed method uses geometric context to detect an object, it is robust to the object’s visual appearance change. As a result, it is ideal for detecting structural objects (e.g., stairways, doorways, etc.). In addition, it has high scalability and parallelism. The method is also capable of detecting some indoor non-structural objects. Experimental results demonstrate that the proposed method has a high success rate in object recognition.
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