Automatic road extraction from lidar data based on height fitting difference

In this paper, a new method for automatic detection of roads from Light Detection and Ranging (LIDAR) data is presented. A morphological filter and an elevation difference threshold are first combined to classify the original data. In the following step, a height fitting difference algorithm is introduced and performed to calculate height fitting difference with a multi-direction template for each pixel. The algorithm acquires two pieces of information about roads: the least squares fitting difference and the corresponding orientation. Then, the Otsu's method is applied to obtain a road map with the fitting difference feature. After performing the Euclidean distance transform on the segmented road map, road centerlines are searched in the distance map. Next, the centerlines are connected and optimized so that long and smooth road centerlines are obtained. Finally, road boundaries are found by setting a proper width value for each road centerline. The proposed method has been tested on various complicated urban images. Experimental results demonstrate that our new method works efficiently and correctly.