An analytical approach to evaluate point cloud registration error utilizing targets

Abstract

Point cloud registration is essential for processing terrestrial laser scanning 16 (TLS) point cloud datasets. The registration precision directly in uences 17 and determines the practical usefulness of TLS surveys. However, in terms 18 of target based registration, analytical point cloud registration error models 19 employed by scanner manufactures are only suitable to evaluate target regis20 tration error, rather than point cloud registration error. This paper proposes 21 an new analytical approach called the registration error (RE) model to di22 rectly evaluate point cloud registration error. We verify the proposed model 23 by comparing RE and root mean square error (RMSE) for all points in 24 three point clouds that are approximately equivalent.

Establishment of a New Quantitative Evaluation Model of the Targets’ Geometry Distribution for Terrestrial Laser Scanning

“…For these reasons, we will first introduce the common registration model of two scans. We will then present the calculation of rotation parameters using the Rodrigues matrix [3,6]. Thirdly, we will propose a new calculation method of the translation parameter (similar to spatial distance resection in GPS [22]), which can ensure that the parameters of translation and rotation are computed independently.…”

“…In the context of TLS, registration is the transformation of multiple point clouds (scans) into the coordinate system of a chosen scan [2]. The rigid body transformation operation of registration is expressed in Equation (1), in which the point clouds in Scan i + 1 are transformed into Scan i using the three translation parameters t x , t y , and t z and the three rotation parameters r a , r b , and r c [3,23].…”

“…If the approximation values of translation parameters and corrections of translation parameters t x , t y , and t z are t x,0 , t y,0 , and t z,0 (calculated by the method of Appendix C in [3]) and ε t x , ε t y , and ε t z , then from the linearization theorem [22,24], the linearization form of Equation (10) can be expressed as…”

“…The spatial resolution of the data is much higher than that of conventional surveying methods [1]. Due to occluded surfaces and limitations in the view of a scanner, we usually need to make several scans from different setups of the scanner in order to survey a quite large and complex object [2,3]. These point clouds (scans) must first be registered to a chosen coordinate system before a coherent parametric description of the object can be formed [4].…”

“…Although target-based registration technology is relatively mature [7], studies of the target-based registration precision are still required, such as those on measurement improvement [8,9], the uncertainty of the target center estimation [10], the error propagation for two scans and multiple scans [3,4,11], the directly geo-referenced TLS data precision [3,12], the relationship between the registration precision and the rotation and translation matrices [13], the relationship between the registration precision and the targets' geometry distribution (TGD) [2], etc. For the relationship between the registration precision and TGD, some researchers have found that an increase in the number of targets can improve the registration precision, and think that the targets will be distributed evenly and will not lie on the same line or be close to such a configuration [2,[14][15][16][17][18][19][20].…”