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

2. Outline • Introduction • Using In-Situ Conjugate Features • Weighted NISLT Approach • Quality Assessment • Numerical Validation • Conclusion NTUCvE Surveying Engineering Group

3. Introduction • Light Detection and Ranging (LIDAR) is capable of acquiring 3D spatial information in a fast and automatic manner. • Can be equipped on platforms of various kinds (air-borne, mobile, and terrestrial). • 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

4. Introduction (cont’d) • Incompleteness due to obstructions Many obstructions could occur when the LIDAR point cloud is collected from a single station. Only partial information is acquired for the 3D object.

5. Introduction (cont’d) • Error magnification due to projective geometry Point coordinates are based on range and angular measurements both of which contain errors. As a result, the quality will become lower for outer regions.

6. Introduction (cont’d) • Registration of LIDAR datasets from multiple stations Each dataset is defined in an arbitrary local reference frame. A 3D similarity transformation model is usually postulated to relate the datasets defined in different reference frames. 1 2 2 1 2 s: scale R: rotation matrix t: translation vector 1 Station 1 Station 2

7. Using In-Situ Features Obtaining the transformation parameters • Classic approach: point-based least-squares approach • Find (>=3) conjugate points in two LIDAR datasets • Perform least-squares parameter estimations • Requires 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.

8. Using In-Situ Features Obtaining the transformation parameters • Proposed approach: using directly in-situ features • Extended feature types • Definite features • Points: vectors between points • Lines: directional vectors • Planar patches: normal vectors • Indefinite features • Groups of points: eigenvectors of • the tensor field constructed by a • group of point. With these extended feature types, it becomes possible to use the geometric components that are already inherent in the scanned object.

9. Using In-Situ Features • In-situ features usable for LIDAR dataset registrations Highway surfaces Bridge pillars Slope surfaces and edges Structure edges and rails No need to set up control targets  reduce the cost for field work.

10. Weighted NISLT Approach • Once feature correspondence is established, the transformation parameters are estimated by the weighted NISLT (Non-Iterative Solutions for Linear Transformations) technique: Scale parameter where dxij and dx’ij are coordinate differences (vectors) in the original and transformed systems, is the weight matrix, lkis a kx1 unity vector.

11. 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

12. Weighted NISLT Approach • Characteristics of weighted NISLT approach • - Closed-form solution, requires no initial • values nor iterative computations  • highly efficient compared to • LSQ-based approaches. • - Weighted parameter estimation model  uncertainties of input • observables can be realistically taken into consideration. • - Accepts input observables of different kinds (e.g. vectors between • points, directional vectors of linear features, normal vectors of • planar features, and eigenvectors of groups of points)  make • possible a direct use of various in-situ geometric features.

13. Quality Assessment • Classical point-based approach: • Registration quality is typically evaluated by the post-fit residuals for point coordinates after applying the estimated parameters. : post-fit residual vector of point i n : number of conjugate points This index gives a vague interpretation on the obtained result since it represents only the positional agreement between two datasets  geometrical similarity is not considered!!

14. Quality Assessment • Proposed approach: • Here features of various kinds are used for a registration. The quality is then evaluated based on the following two indexes: Absolute Consistency (qa) Relative Similarity (qr) Positional alignment Geometric similarity : post-fit residual vector of conjugate point i or the vector between point i ‘s projected points on two conjugate features. : the angle between two conjugate vectors (directional vectors, normal vectors, or eigenvectors) after the registration. : the numbers of conjugate points and conjugate vectors

15. (a) (b) (c) (d) Quality Assessment • Interpretation of a registration solution: Moderate qa, good qr. Moderate qa and qr. Poor qa, good qr. Poor qa and qr. The quality of a registration solution can be explicitly defined by the proposed two indexes qa and qr.

16. S2 S1 Numerical Validation • Data collection: A case study was performed for a 250m-long reinforced concrete (RC) bridge in Taipei City. Two LIDAR stations (S1, S2) were set up about 80m away from the bridge.

17. Numerical Validation • Data collection (cont’d): LIDAR point cloud was collected at each station using a Trimble® GS200 Terrestrial Laser Scanner. Resolution for the scanned points of the bridge was roughly between 0.02m ~ 0.04m. No control sphere or reflective stick was set up in the scanned area. • TrimbleGS200 Laser Scanner • - Range: 2m~200m • - Accuracy: range = 6 mm @ 100 m • angular = 6 mm @ 100 m • - Max. Density: 3mm@100m

18. Numerical Validation • Collected datasets and in-situ features used for registration Two sets of LIDAR point clouds were collected at the two stations. Since no control point was available, in-situ features were selected from the datasets and used for a registration. Two pillars, a rail and a beam surface were used as conjugate features. Station 1 Station 2

19. Numerical Validation • NISLT registration The eigenvectors of conjugate features were used as observables while solving for the transformation parameters based on the proposed weighted NISLT approach. Station 1 Station 2

20. Numerical Validation • Registration results (integrated point clouds) Shown in true colors Shown in blue for points collected at station 1 and in red for points collected at station 2

21. Numerical Validation • Registration results (integrated point clouds) S2 S1 Integrated

22. Numerical Validation • Registration results (quality assessment) • Absolute consistency (qa) = 3.81cm. • Relative similarity (qr) = 1.864e-4 . • qr is equivalent to a 3.73cm positional distortion for an object of • size 200m. Equally accurate in terms of positional agreement and • geometric similarity. • Both values are within a reasonable range considering the • 2cm~4cm resolution of the original LIDAR datasets  the • registration quality is mostly dependent on the point resolution in • this case.

23. Conclusion • The proposed approach increases the number of usable features for a registration solution  the cost for LIDAR field work can be significantly reduced. • The weighted NISLT enables an efficient parameter estimation when in-situ hybrid conjugate features are used. • The two quality indexes (absolute consistency and relative similarity) give a complete and explicit quality indication for a registration solution. • An automatic approach for selecting qualified in-situ features should be developed in the future.

24. Thanks for your attention For more information, please contact: Jen-Yu Han, Ph.D. Department of Civil Engineering, National Taiwan University Email: jyhan@ntu.edu.tw Phone: +886-2-33664347 Website: http://homepage.ntu.edu.tw/~jenyuhan