QUALITY ASSESSMENT FOR LIDAR POINT CLOUD REGISTRATION USING IN-SITU CONJUGATE FEATURES

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

NTUCvE Surveying Engineering Group Outline ,[object Object]

Using In-Situ Conjugate Features

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

Introduction (cont’d) ,[object Object],Many obstructions could occur when the LIDAR point cloud is collected from a single station. Only partial information is acquired for the 3D object.

Introduction (cont’d) ,[object Object],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.

Introduction (cont’d) ,[object Object],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

Using In-Situ Features Obtaining the transformation parameters ,[object Object]

Find (>=3) conjugate points in two LIDAR datasets

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.

Using In-Situ Features Obtaining the transformation parameters ,[object Object], Extended feature types ,[object Object], Points: vectors between points Lines: directional vectors Planar patches: normal vectors ,[object Object], 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.

Using In-Situ Features ,[object Object],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.

Weighted NISLT Approach ,[object Object],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.

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

Weighted NISLT Approach ,[object Object], - 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.

Quality Assessment ,[object Object],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!!

Quality Assessment ,[object Object],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

(a) (b) (c) (d) Quality Assessment ,[object Object],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.

S2 S1 Numerical Validation ,[object Object],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.

QUALITY ASSESSMENT FOR LIDAR POINT CLOUD REGISTRATION USING IN-SITU CONJUGATE FEATURES

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QUALITY ASSESSMENT FOR LIDAR POINT CLOUD REGISTRATION USING IN-SITU CONJUGATE FEATURES