EXTENDED RANSAC ALGORITHM FOR AUTOMATIC DETECTION OF BUILDING ROOF PLANES FROM LIDAR DATA

Airborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial importance. For this purpose, this paper studies the Random Sample Consensus (RANSAC) algorithm. Its principle and pseudocode - seldom detailed in the related literature - as well as its complete analyse are presented in this paper. Despite all advantages of this algorithm, it gives sometimes erroneous results. That can be explained by the fact that it uses a pure mathematical principle for detecting the roof planes. So it looks for the best plane without taking into account the particularity of the captured object. The extended RANSAC algorithm proposed in this paper allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof. It is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity. Moreover, the adjacency relationships of the neighbouring roof planes are described and analysed. Hence the roof planes are successfully detected and adjacency relationships of the adjacent roof planes are calculate. Finally the automatic building modelling can be carried out easily.

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