QUALITY ASSESSMENT FOR LIDAR POINT CLOUD REGISTRATION USING IN-SITU CONJUGATE FEATURES
1. IGARSS, 24-29 July 2011, Vancouver, Canada (Session FR2.T03) Quality Assessment for LIDAR Point Cloud Registration using In-Situ Conjugate Features Jen-Yu Han1, Hui-Ping Tserng1, Chih-Ting Lin2 1 Department of Civil Engineering, National Taiwan University 2 Graduate Institute of Electronics Engineering, National Taiwan University
8. Can be equipped on platforms of various kinds (air-borne, mobile, and terrestrial).
9. Usually requires multiple scans in order to construct a complete and accurate 3D model.Reason 1: Incompleteness due to obstructions Reason 2: Error magnification due to projective geometry
10.
11.
12.
13.
14. Find (>=3) conjugate points in two LIDAR datasets
15. Perform least-squares parameter estimationsRequires extra effort to set up identifiable targets (e.g. control spheres or reflective sticks) or perform feature extractions. Requires a set of good initial values and iterative computations to obtain reliable parameter estimates.
16.
17.
18.
19. Weighted NISLT Approach Rotational parameters where ΔX and ΔX’ are the matrices by stacking all the normalized row vectors in the original and transformed systems. Translational parameters
35. The weighted NISLT enables an efficient parameter estimation when in-situ hybrid conjugate features are used.
36. The two quality indexes (absolute consistency and relative similarity) give a complete and explicit quality indication for a registration solution.