Real-time ground filtering algorithm of cloud points acquired using Terrestrial Laser Scanner (TLS)

• Published in: International journal of applied earth observation and geoinformation 2021-12, Vol.105, p.102629, Article 102629
• Main Authors: Diaz, Nelson, Gallo, Omar, Caceres, Jhon, Porras, Hernan
• Format: Article
• Language: English
• Subjects: algorithms; Ground filter; Normal vector; PCA; scanners; spatial data; TLS; Voxel
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2021.102629

•Fast and precise ground filtering is a key problem in point cloud understanding.•K-nearest neighbor search is usually a bottleneck algorithm of ground filtering.•Kd-tree and parallel processing boost the k-nearest neighbor search.•Principal component analysis and the RANSAC algorithm are potent tools to find flat regions.•A voxel structure might reduce the computation time or nearest neighbors. 3D modeling based on point clouds requires ground-filtering algorithms that separate ground from non-ground objects. This study presents two ground filtering algorithms. The first one is based on normal vectors. It has two variants depending on the procedure to compute the k-nearest neighbors. The second algorithm is based on transforming the cloud points into a voxel structure. To evaluate them, the two algorithms are compared according to their execution time, effectiveness and efficiency. Results show that the ground filtering algorithm based on the voxel structure is faster in terms of execution time, effectiveness, and efficiency than the normal vector ground filtering.