Hierarchical registration of laser point clouds between airborne and vehicle-borne data considering building eave attributes.

Laser point cloud registration is a key step in multisource laser scanning data fusion and application. Aimed at the problems of fewer overlapping regional features and the influence of building eaves on registration accuracy, a hierarchical registration algorithm of laser point clouds that considers building eave attributes is proposed in this paper. After extracting the building feature points of airborne and vehicle-borne light detection and ranging data, the similarity measurement model is constructed to carry out coarse registration based on pseudo-conjugate points. To obtain the feature points of the potential eaves (FPPE), the building contour lines of the vehicle-borne data are extended using the direction prediction algorithm. The FPPE data are regarded as the search set, in which the iterative closest point (ICP) algorithm is employed to match the true conjugate points between the airborne laser scanning data and vehicle-borne laser scanning data. The ICP algorithm is used again to complete the fine registration. To evaluate the registration performance, the developed method was applied to the data processing near Shandong University of Science and Technology, Qingdao, China. The experimental results showed that the FPPE dataset can effectively address the coarse registration accuracy effects on the convergence of the iterative ICP. Before considering eave attributes, the mean registration errors (MREs) of the proposed method in the xoz plane, yoz plane, and xoy plane are 0.318, 0.96, and 0.786 m, respectively. After considering eave attributes, the MREs decrease to 0.129, 0.187, and 0.169 m, respectively. The developed method can effectively improve the registration accuracy of the laser point clouds, which not only solves the problem of matching true conjugate points under the effects of the eaves but also avoids converging to a local minimum due to ICP's poor coarse registration.

[1]  O. Sass,et al.  Combining airborne and terrestrial laser scanning for quantifying erosion and deposition by a debris flow event , 2012 .

[2]  Clive S. Fraser,et al.  An energy minimization approach to automated extraction of regular building footprints from airborne LiDAR data , 2014 .

[3]  Jen-Jer Jaw,et al.  Multi-Feature Registration of Point Clouds , 2017, Remote. Sens..

[4]  Jungho Im,et al.  Road Extraction: A Review of LiDAR-Focused Studies , 2013 .

[5]  Thomas Blaschke,et al.  Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art , 2015, Remote. Sens..

[6]  Yanming Chen,et al.  Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud , 2015, Remote. Sens..

[7]  M. Bosse,et al.  Automatic segmentation of 3D laser point clouds by ellipsoidal region growing , 2009, ICRA 2009.

[8]  Shaobo Xia,et al.  Improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data based on a multiscale cylindrical neighborhood. , 2020, Applied optics.

[9]  A. Large.  Laser Scanning for the Environmental Sciences , 2009 .

[10]  Kresimir Kusevic,et al.  INTEGRATION OF TERRESTRIAL AND AIRBORNE LIDAR DATA FOR SYSTEM CALIBRATION , 2008 .

[11]  Michael Becht,et al.  From geotechnical analysis to quantification and modelling using LiDAR data: a study on rockfall in the Reintal catchment, Bavarian Alps, Germany , 2012 .

[12]  Quan Li,et al.  Automatic Registration of Terrestrial and Airborne Point Clouds Using Building Outline Features , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Bisheng Yang,et al.  An automated method to register airborne and terrestrial laser scanning point clouds , 2015 .

[14]  Bisheng Yang,et al.  Automatic registration of large-scale urban scene point clouds based on semantic feature points , 2016 .

[15]  Ziyin Wu,et al.  Filtering of airborne LiDAR bathymetry based on bidirectional cloth simulation , 2020 .

[16]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[17]  A. Gruen,et al.  Least squares 3D surface and curve matching , 2005 .

[18]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[19]  Salvatore Stramondo,et al.  Combined use of ground-based systems for Cultural Heritage conservation monitoring , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[20]  Manchun Li,et al.  Semi-Automatic Registration of Airborne and Terrestrial Laser Scanning Data Using Building Corner Matching with Boundaries as Reliability Check , 2013, Remote. Sens..

[21]  Yanming Chen,et al.  Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data , 2015, Remote. Sens..

[22]  Xiao Hua Wang,et al.  Propagated Uncertainty Models Arising From Device, Environment, and Target for a Small Laser Spot Airborne LiDAR Bathymetry and its Verification in the South China Sea , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[23]  George Vosselman,et al.  Mapping curbstones in airborne and mobile laser scanning data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Bisheng Yang,et al.  Semiautomated Building Facade Footprint Extraction From Mobile LiDAR Point Clouds , 2013, IEEE Geoscience and Remote Sensing Letters.

[25]  Hao Li,et al.  An Efficient Image Matching Algorithm Based on Adaptive Threshold and RANSAC , 2018, IEEE Access.

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

[27]  Lutz Plümer,et al.  CityGML – Interoperable semantic 3D city models , 2012 .

[28]  Sorin C. Popescu,et al.  Mapping surface fuel models using lidar and multispectral data fusion for fire behavior , 2008 .

[29]  Joseph L. Mundy,et al.  Evaluation of feature-based 3-d registration of probabilistic volumetric scenes , 2014 .

[30]  Derek D. Lichti,et al.  A method for automated registration of unorganised point clouds , 2008 .

[31]  Yue Ma,et al.  Classification of Coral Reefs in the South China Sea by Combining Airborne LiDAR Bathymetry Bottom Waveforms and Bathymetric Features , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Kuldip K. Paliwal,et al.  Fast K-dimensional tree algorithms for nearest neighbor search with application to vector quantization encoding , 1992, IEEE Trans. Signal Process..

[33]  Norbert Haala,et al.  Efficient integration of aerial and terrestrial laser data for virtual city modeling using LASERMAPs , 2005 .

[34]  Tee-Ann Teo,et al.  Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds , 2014, Remote. Sens..

[35]  Hao Xu,et al.  Registration of Laser Scanning Point Clouds: A Review , 2018, Sensors.

[36]  Mohammed Bennamoun,et al.  3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Terje Gobakken,et al.  Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data , 2014 .

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

[39]  J. Hyyppä,et al.  Laser scanning applications in fluvial studies , 2011 .

[40]  José Luis Lerma,et al.  Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier , 2014 .

[41]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Joanne C. White,et al.  Lidar sampling for large-area forest characterization: A review , 2012 .