Fast Vision-Based Minimum Distance Determination Between Known and Unknown Objects

Titelangaben

Kuhn, Stefan ; Henrich, Dominik:
Fast Vision-Based Minimum Distance Determination Between Known and Unknown Objects.
In: IEEE/RSJ International Conference on Intelligent Robots and Systems. - San Diego, CA, USA , 2007 . - S. 2186-2191
DOI: https://doi.org/10.1109/IROS.2007.4399208

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Abstract

We present a method for quickly determining the minimum distance between multiple known and multiple unkown objects within a camera image. Known objects are objects with known geometry, position, orientation, and configuration. Unkown objects are objects which have to be detected by a vision sensor but with unkown geometry, position, orientation and configuration. The known objects are modeled and expanded in 3D and then projected into a camera image. The camera image is classified into object areas including known and unknown objects and into non-object areas. The distance is conservatively estimated by searching for the largest expansion radius where the projected model does not intersect the object areas classified as unknown in the camera image. The method requires only minimal computation times and can be used for surveillance and safety applications.