Planar surface detection for sparse and heterogeneous mobile laser scanning point clouds

Abstract

Abstract Plane detection and segmentation is one of the most crucial tasks in point cloud processing. The output from this process can be used as input for further processing steps, such as modelling, registration and calibration. However, the sparseness and heterogeneity of Mobile Laser Scanning (MLS) point clouds may lead to problems for existing planar surfaces detection and segmentation methods. This paper proposes a new method that can be applicable to detect and segment planar features in sparse and heterogeneous MLS point clouds. This method utilises the scan profile patterns and the planarity values between different neighbouring scan profiles to detect and segment planar surfaces from MLS point clouds. The proposed method is compared to the three most state-of-the-art segmentation methods (e.g. RANSAC, a robust segmentation method based on robust statistics and diagnostic principal component analysis – RDCPA as well as the plane detection method based on line arrangement). Three datasets are used for the validation of the results. The results show that our proposed method outperforms the existing methods in detecting and segmenting planar surfaces in sparse and heterogeneous MLS point clouds. In some instances, the state-of-the-art methods produce incorrect segmentation results for facade details which have a similar orientation, such as for windows and doors within a facade. While RDCPA produces up to 50% of outliers depending on the neighbourhood threshold, another method could not detect such features at all. When dealing with small features such as a target, some algorithms (including RANSAC) were unable to perform segmentation. However, the propose algorithm was demonstrated to detect all planes in the test data sets correctly. The paper shows that these mis-segmentations in other algorithms may lead to significant errors in the registration process of between 1.047 and 1.614 degrees in the angular parameters, whereas the propose method had only resulted in 0.462 degree angular bias. Furthermore, it is not sensitive to the required method parameters as well as the point density of the point clouds.