Vision-based detection of chain link fence obstacles using a deformable template approach

Autonomous and semi-autonomous ground robots exploring urban environments need the ability to detect various types of fences that are obstacles to mobility. Visual detection of wire fences is challenging due to the small size of the wire forming the fence and the presence of multiple unknown natural and/or man-made backgrounds visible through the structure of the fence. A deformable template based algorithm has been developed to visually identify the periodic structure of chain link fences in typical outdoor scenes. The algorithm extracts edge points from the image using the Prewitt gradient operator and a histogram based thresholding method. The fence is modeled as two sets of regularly spaced parallel lines. Each of these sets of lines is parameterized by orientation, line spacing, and location of the left-most line within a specified Region Of Interest. A search in this parameter space finds the template that minimizes an energy function based on proximity of lines in the deformed template to edge points in the images. The algorithm performs well even in the presence of clutter edges from background textures in the scene. Modification of the template to account for effects of perspective distortion when viewing fences from off-normal angles is discussed.