An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds
Published in IEEE Transactions on Intelligent Transportation Systems, 2019
Recommended citation: Xu Sheng, Wang Ruisheng(*), Wang Hao and Zheng Han. "An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds." IEEE Transactions on Intelligent Transportation Systems. vol. 21(7), pp. 2765-2776, 2020, doi: 10.1109/TITS.2019.2912455. https://ieeexplore.ieee.org/document/8705009
This paper aims to propose a new optimal hierarchical clustering approach to 3D mobile light detection and ranging (LiDAR) point clouds. The hierarchical clustering is performed on unorganized point clouds based on a proximity matrix that consists of a distance term and a direction term. In the dissimilarity calculation of two clusters, a pair of points from each of two clusters is selected, respectively, and Euclidean distances between the points are employed to define the distance term. The direction term is obtained by the differences of normal vectors at chosen points. The main contribution is that the cluster combination in the hierarchical clustering is optimized by a point-based graph model. The cluster combination is formulated as a problem of matching, optimized by finding the minimum-cost perfect matching in a bipartite graph. The results show that the proposed hierarchical clustering method succeeds in segmenting object from point clouds without any human-computer interaction and outperforms the state-of-the-art segmentation approaches in terms of completeness and correctness.
Recommended citation: ‘S. Xu, R. Wang(*), H. Wang, H. Zheng. "An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds." IEEE Transactions on Intelligent Transportation Systems. vol. 21(7), pp. 2765-2776, 2020.’