Tree point clouds registration using an improved ICP algorithm based on kd-tree

The light detection and ranging (LiDAR) technology plays an important role in obtaining the three-dimensional information. A large number of point cloud data of the objects can be obtained through the LiDAR technology. The Iterative Closest Point (ICP) algorithm was widely used for registering the point cloud data, which typically only scan an object from one direction at a time. However, massive point cloud data has brought a great number of troubles to this registration method. The k-d tree is similar to the general tree structure and it can store, manage and search data efficiently. Therefore, an improved ICP algorithm which based on k-d tree was presented for tree point cloud data registration in this paper. The results showed that the improved ICP algorithm can improve the speed of registration about 10 times higher, and it also has obvious advantages in accuracy of registration.

[1]  Bin Wang,et al.  Automatic Registration of Tree Point Clouds From Terrestrial LiDAR Scanning for Reconstructing the Ground Scene of Vegetated Surfaces , 2014, IEEE Geoscience and Remote Sensing Letters.

[2]  Martin Beland,et al.  A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR , 2014, Environ. Model. Softw..

[3]  Kenji Omasa,et al.  3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar , 2013 .

[4]  Kourosh Khoshelham,et al.  Localized Registration of Point Clouds of Botanic Trees , 2013, IEEE Geoscience and Remote Sensing Letters.

[5]  Kenji Omasa,et al.  Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Joachim Hertzberg,et al.  Cached k-d tree search for ICP algorithms , 2007, Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007).

[7]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[8]  Zheng Niu,et al.  Height Extraction of Maize Using Airborne Full-Waveform LIDAR Data and a Deconvolution Algorithm , 2015, IEEE Geoscience and Remote Sensing Letters.

[9]  Yifang Ban,et al.  Toward an Optimal Algorithm for LiDAR Waveform Decomposition , 2012, IEEE Geoscience and Remote Sensing Letters.

[10]  Zheng Niu,et al.  Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping. , 2015, Optics express.

[11]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[12]  Michael A. Greenspan,et al.  Approximate k-d tree search for efficient ICP , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[13]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.