Point-to-plane registration of terrestrial laser scans

Grant, Darion; Bethel, James; Crawford, Melba M.

doi:10.1016/j.isprsjprs.2012.05.007

Cited by 69 publications

(46 citation statements)

References 19 publications

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“…The mean misclosure for the P2P algorithm was 0.80mm whilst that for the ICP algorithm was 5.39mm. Furthermore, Grant et al (2012) state that the P2P registration approach was tested on both simulated and real TLS data and that experimental results showed it to be four times more accurate than the registration approach of Chen and Medioni (1992).…”

Section: Registration and Georeferencing In Terrestrial Laser Scanningmentioning

confidence: 99%

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

et al. 2016

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Section: Registration and Georeferencing In Terrestrial Laser Scanningmentioning

confidence: 99%

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

et al. 2016

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show abstract

“…Beside comparing the result of the least square plane fitting adjustment process using different input, this paper also compares the results of plane based fitting with point to plane approaches (Grant et al, 2012;Takai et al, 2013). According to Nguyen et al (2016) the three nearest points point of the points are normally on the same scan line.…”

Section: Iterative Closest Pointmentioning

confidence: 99%

“…According to Nguyen et al (2016) the three nearest points point of the points are normally on the same scan line. Consequently, the point to plane approaches proposed by Grant et al (2012) is not suitable for MLS point clouds. Hence, our paper adapts the point to plane approach proposed by Takai et al (2013) Table 4 RMSs of the points fitted onto their models before and after least square adjustment process by using point to plane…”

Section: Iterative Closest Pointmentioning

confidence: 99%

Comparative Study of Automatic Plane Fitting Registration for MLS Sparse Point Clouds With Different Plane Segmentation Methods

Nguyễn¹,

Belton²,

Helmholz³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The least square plane fitting adjustment method has been widely used for registration of the mobile laser scanning (MLS) point clouds. The inputs for this process are the plane parameters and points of the corresponding planar features. These inputs can be manually and/or automatically extracted from the MLS point clouds. A number of papers have been proposed to automatically extract planar features. They use different criteria to extract planar features and their outputs are slightly different. This will lead to differences in plane parameters values and points of the corresponding features. This research studies and compares the results of the least square plane fitting adjustment process with different inputs obtained by using different segmentation methods (e.g. RANSAC, RDPCA, Cabo, RGPL) and the results from the point to plane approach -an ICP variant. The questions for this research are: (1) which is the more suitable method for registration of MLS sparse point clouds and (2) which is the best segmentation method to obtain the inputs for the plane based MLS point clouds registration? Experiments were conducted with two real MLS point clouds captured by the MDL -Dynascan S250 system. The results show that ICP is less accurate than the least square plane fitting adjustment. It also shows that the accuracy of the plane based registration process is highly correlated with the mean errors of the extracted planar features and the plane parameters. The conclusion is that the RGPL method seems to be the best methods for planar surfaces extraction in MLS sparse point clouds for the registration process.

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“…Other types of correspondences, such as planes (Brenner and Dold, 2007;Khoshelham and Gorte, 2009;Khoshelham, 2010), point-planes (Sande et al, 2010;Grant et al, 2012) and lines (Bucksch and Khoshelham, 2013;dos Santos et al, 2013), have been used for registering laser scanner point clouds. Taguchi …”

Section: Related Workmentioning

confidence: 99%

Generation and weighting of 3D point correspondences for improved registration of RGB-D data

Khoshelham

Santos

Vosselman

2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Registration of RGB-D data using visual features is often influenced by errors in the transformation of visual features to 3D space as well as the random error of individual 3D points. In a long sequence, these errors accumulate and lead to inaccurate and deformed point clouds, particularly in situations where loop closing is not feasible. We present an epipolar search method for accurate transformation of the keypoints from 2D to 3D space, and define weights for the 3D points based on the theoretical random error of depth measurements. Our results show that the epipolar search method results in more accurate 3D correspondences. We also demonstrate that weighting the 3D points improves the accuracy of sensor pose estimates along the trajectory.

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Point-to-plane registration of terrestrial laser scans

Cited by 69 publications

References 19 publications

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Comparative Study of Automatic Plane Fitting Registration for MLS Sparse Point Clouds With Different Plane Segmentation Methods

Generation and weighting of 3D point correspondences for improved registration of RGB-D data

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