In this paper, we focus our discussion on auto-extraction of linear features like guard-rails (a fence line at the edge of the road or middle of the road) from vehicle-borne laser data. The vehicle-borne laser data is quite heterogeneous in nature as we scan the objects while the vehicle is moving. In order to extract, linear features, the laser data are projected on the horizontal plane and then rasterized. The raster data contains grid density image and maximum height image, which are used for assisting in decision-making process for linear features. The raster data is further converted to binary image using threshold values for linear features. Radon transformation is applied on the binary image to identify the seed position and orientation of the most probable linear features. Arbitrary seed lines are drawn from these seed points. These seed points (and lines) coordinate information are then converted back to the vector data (original laser points). A circle growing technique is applied on the seed points to correct the seed position of the linear feature points at certain horizontal spacing. Once all the seed points are corrected on the original data, straight lines are fitted (locally) to represent the linear features. The height of the linear feature is computed by fitting the maximum height values of the points that fall inside the circle (during the circle growing process). This gives us 3-D modeling of linear features. It is possible to identify linear features from vehicle-borne laser data. The algorithm is successful in extracting the linear features automatically for continuous linear features. If the linear features are non-continuous (or smaller spans of a few meters) or data are occluded, auto-extraction will be quite complex and might even fail to identify. In this case, a semiautomated extraction is recommended.
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