Characterizing Indoor Vanishing Points by Local Dominant Orientation Signature from Omnidirectional Vision
For a mobile robot, it is necessary to have the ability to move accurately and autonomously in an known and/or unknown environment. The vanishing points existing in indoor environment are a key factor for the success of robot localization and navigation. To successfully detect these vanishing points, this paper proposes a novel approach of utilizing the distribution of orientation information from omnidirectional vision. The local orientation data are first computed by proper edge detection and then characterized by the local dominant orientation signature descriptors. These descriptors are further filtered based on the strength of its own orientation as well as its neighboring orientations. The final orientation field is constructed by using the interpolation of radial basis functions. The proposed approach of vanishing point detection has been extensively tested in various indoor environments such as narrow pathway library and multi-path corridor. Experimental results demonstrate good performance compared with existing approach.
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