Adjustment of Discrepancies Between LIDAR Data Strips Using Linear Features

Despite the recent developments in light detection and ranging systems, discrepancies between strips on overlapping areas persist due to the systematic errors. This letter presents an algorithm that can be used to detect and adjust such discrepancies. To achieve this, extracting conjugate features from the strips is a prerequisite step. In this letter, linear features are chosen as conjugate features because they can be accurately extracted from man-made structures in urban area and more easily extracted than the point features. Based on such a selection strategy, a simple and robust algorithm is proposed that is generally applicable for extracting such features. The algorithm includes methods that can be used to establish observation equations from similarity measurements of the extracted features. Then, several transformations are selected and used to adjust the strips. Following the transformation, the fitness of linear features is tested to determine whether the discrepancies have been resolved; the results are then evaluated statistically. The results demonstrate that the algorithm is effective in reducing the discrepancies between the strips.