Fast and Automatic Image-based Registration of TLS Data

Martin Weinmann, Michael Weinmann, Stefan Hinz und Boris Jutzi
In: ISPRS Journal of Photogrammetry and Remote Sensing (Okt. 2011), 66:6, Supplement(62-70)
 

Abstract

The fast and automatic registration of laser scanner data is of great interest in photogrammetric research. Recent developments show that for registration purposes characteristic 3D points can be extracted from the measured laser data, where the data are also represented as image. In this paper, radiometric and geometric information derived from TLS data are utilized for estimating the transformation parameters between two unregistered point clouds. After the extraction of characteristic 2D points based on SIFT features, these points are projected into 3D space by using interpolated range information. From these 3D conjugate points and their corresponding 2D projections onto a virtual plane 3D-to-2D correspondences are established. The fast, accurate and robust RANSAC-based registration scheme including the EPnP algorithm provides a framework to estimate the coarse transformation parameters from these 3D-to-2D correspondences. The coarse estimates are further refined by a single step outlier removal to gain a higher accuracy by introducing additional geometric constraints. These new constraints are based on 3D-to-3D correspondences which are much stronger than the 3D-to-2D correspondences alone. It will be shown that the presented approach is successfully applied to a benchmarked data set with millions of points resulting in a fast and accurate estimation of the transformation parameters with a processing speed of several seconds on a standard PC and an accuracy in the low centimeter range.

Stichwörter: Automation, Image, Laser Scanning, point cloud, Registration, TLS

Bilder

Bibtex

@ARTICLE{weinmann-2011-RegistrationOfTLS,
    author = {Weinmann, Martin and Weinmann, Michael and Hinz, Stefan and Jutzi, Boris},
     pages = {62--70},
       key = {citeulike:9917141},
     title = {Fast and Automatic Image-based Registration of TLS Data},
   journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    volume = {66},
    number = {6, Supplement},
      year = {2011},
     month = oct,
      note = {Advances in LIDAR Data Processing and Applications},
  keywords = {Automation, Image, Laser Scanning, point cloud, Registration, TLS},
  abstract = {The fast and automatic registration of laser scanner data is of great interest in photogrammetric
              research. Recent developments show that for registration purposes characteristic 3D points can be
              extracted from the measured laser data, where the data are also represented as image. In this paper,
              radiometric and geometric information derived from TLS data are utilized for estimating the
              transformation parameters between two unregistered point clouds. After the extraction of
              characteristic 2D points based on SIFT features, these points are projected into 3D space by using
              interpolated range information. From these 3D conjugate points and their corresponding 2D
              projections onto a virtual plane 3D-to-2D correspondences are established. The fast, accurate and
              robust RANSAC-based registration scheme including the EPnP algorithm provides a framework to
              estimate the coarse transformation parameters from these 3D-to-2D correspondences. The coarse
              estimates are further refined by a single step outlier removal to gain a higher accuracy by
              introducing additional geometric constraints. These new constraints are based on 3D-to-3D
              correspondences which are much stronger than the 3D-to-2D correspondences alone. It will be shown
              that the presented approach is successfully applied to a benchmarked data set with millions of
              points resulting in a fast and accurate estimation of the transformation parameters with a
              processing speed of several seconds on a standard PC and an accuracy in the low centimeter range.},
      issn = {0924-2716},
       doi = {10.1016/j.isprsjprs.2011.09.010}
}