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

Khoshelham, Kourosh; Santos, Daniel Rodrigues dos; Vosselman, G.

doi:10.5194/isprsannals-ii-5-w2-127-2013

Cited by 25 publications

(26 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For future work, it would be desirable to compare different approaches for point cloud registration on a benchmark dataset and to point out pros and cons of these approaches in order to allow end-users to select an appropriate method according to their requirements. Furthermore, it might be advisable to introduce a weighting of feature correspondences which may principally be based on different constraints Khoshelham et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Geometric Point Quality Assessment for the Automated, Markerless and Robust Registration of Unordered TLS Point Clouds

Weinmann

Jutzi

2015

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

View full text Add to dashboard Cite

ABSTRACT:The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation or urban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systems by capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinate frame. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projective scan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratio of inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. This consistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with a reasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset and show that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers.

show abstract

Section: Discussionmentioning

confidence: 99%

Geometric Point Quality Assessment for the Automated, Markerless and Robust Registration of Unordered TLS Point Clouds

Weinmann

Jutzi

2015

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

View full text Add to dashboard Cite

show abstract

“…According Khoshelham et al (2013) since the Kinect depth images are captured typically at a frame rate of 20 to 30 fps, we can approximate our observation equations with v i = x i,j-1 -x i,j , for which the weight can be defined inversely proportional to the variance of the observation, as follows: (8) where = variance of point x and k is an arbitrary constant.…”

Section: Pairwise Registrationmentioning

confidence: 99%

“…Ataer-Cansizoglu et al (2013) presented a SLAM system that exploits planes in conjunction with points, as primitives in order to minimize failure cases, due to texture less regions. In Khoshelham et al (2013) a weighting scheme to adjust the contribution of the 3D point correspondences for estimation of the transformation parameters is proposed. The obtained results demonstrated that weighting the 3D points improves the accuracy of sensor pose estimation along the trajectory.…”

Section: Related Workmentioning

confidence: 99%

“…Dos Santos et al (2016) presented an adaptive coarse-to-fine registration method for 3D indoor mapping using RGB-D data. They have weighted the 3D points based on the theoretical random error of depth measurements, such as in Khoshelham et al (2013)They also have introduced a novel disparity-based model for an accurate and robust coarse-to-fine registration.…”

Section: Related Workmentioning

confidence: 99%

“…As the shift between the coordinates of the conjugate points in the RGB frame and depth image has a large variance, even when the image coordinates are corrected for the lens distortions, we use the method proposed by Khoshelham et al (2013) to generate 3D correspondences from the 2D inliers.…”

Section: D Point Matching Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

A Weighted Closed-Form Solution for RGB-D Data Registration

Vestena¹,

Santos²,

Jr.³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

ABSTRACT:Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.

show abstract

Point Cloud Registration

Weinmann

2016

Reconstruction and Analysis of 3D Scenes

View full text Add to dashboard Cite

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

Cited by 25 publications

References 32 publications

Geometric Point Quality Assessment for the Automated, Markerless and Robust Registration of Unordered TLS Point Clouds

Geometric Point Quality Assessment for the Automated, Markerless and Robust Registration of Unordered TLS Point Clouds

A Weighted Closed-Form Solution for RGB-D Data Registration

Point Cloud Registration

Contact Info

Product

Resources

About