Robust point cloud registration based on topological graph and Cauchy weighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si56.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>-norm

Li, Jiayuan; Zhao, Pengcheng; Ai, Mingyao

doi:10.1016/j.isprsjprs.2019.12.008

Cited by 33 publications

(18 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the outlier rates of the initial 3D correspondences are very high (> 96% in these scan pairs). We first use an edge voting strategy (Li et al, 2020b) to filter some outliers before applying the proposed method for rigid model estimation. The 3D matching results are shown in Figure 10.…”

Section: Point Cloud Registration Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Robust Estimation in Robot Vision and Photogrammetry: A New Model and Its Applications

Zhang

2021

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

View full text Add to dashboard Cite

Abstract. Robust estimation (RE) is a fundamental issue in robot vision and photogrammetry, which is the theoretical basis of geometric model estimation with outliers. However, M-estimations solved by iteratively reweighted least squares (IRLS) are only suitable for cases with low outlier rates (< 50%); random sample consensus (RANSAC) can only obtain approximate solutions. In this paper, we propose an accurate and general RE model that unifies various robust costs into a common objective function by introducing a “robustness-control” parameter. It is a superset of typical least-squares, l1-l2, Cauchy, and Geman-McClure estimates. We introduce a parameter-decreasing strategy into the IRLS to optimize our model, called adaptive IRLS. The adaptive IRLS begins with a least-squares estimate for initialization. Then, the “robustness-control” parameter is decreased along with iterations so that the proposed model acts as different robust loss functions and has different degrees of robustness. We also apply the proposed model in several important tasks of robot vision and photogrammetry, such as line fitting, feature matching, image orientation, and point cloud registration (scan matching). Extensive simulated and real experiments show that the proposed model is robust to more than 80% outliers and preserves the advantages of M-estimations (fast and optimal). Our source code will be made publicly available in https://ljy-rs.github.io/web.

show abstract

Section: Point Cloud Registration Experimentsmentioning

confidence: 99%

“…For example, generalized pbMestimator (GpbM-estimator) (Mittal et al, 2012) proposes a robust errors-in-variables model. Q-norm estimator (Li et al, 2016) and weighted q-norm estimator (Li et al, 2020b) further improve the robustness by introducing a lq-norm (0 < q < 1) model. These methods are robust to more than 70% outliers.…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation in Robot Vision and Photogrammetry: A New Model and Its Applications

Zhang

2021

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

View full text Add to dashboard Cite

show abstract

“…Li et al first carried out a rough registration of point clouds using a weighted l q -norm and then completed the process of fine registration by using topology diagram theory. This algorithm is considered to have good stability [19]. Based on the registration algorithm of the fast point feature histogram, Wu optimized the neighborhood density of points to complete the coarse registration process and ultimately carried out fine registration with ICP to improve the registration accuracy of the point cloud [20].…”

Section: Preprocessing Of Point Cloudsmentioning

confidence: 99%

“…Segment the point cloud data p i (x i , y i , z i , i, j, k, MC i , ρ i ) by FCM as Equations ( 13) to (19); Simplify point cloud data as bounding box algorithm and Equation (20); Output the simplified point cloud data.…”

Section: Endmentioning

confidence: 99%

An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data

Yang

2020

Applied Sciences

View full text Add to dashboard Cite

To further improve the efficiency and accuracy of the vehicle part inspection process, this paper designs an accurate and efficient vehicle body part inspection framework based on scattered point cloud data (PCD). Firstly, a hybrid filtering algorithm for point cloud denoising is designed to solve the problem of multiple noise points in the original point cloud measurement data. Secondly, a point cloud simplification algorithm based on Fuzzy C-Means (FCM) is designed to solve the problems of a large amount of data and many redundant points in the PCD. Thirdly, a point cloud fine registration algorithm based on the Teaching-Learning-based Optimization (TLBO) algorithm is designed to solve the problem where the initial point cloud measurement data cannot be located properly. Finally, the deviation distance between the PCD and Computer-Aided-Design (CAD) model is calculated by the K-Nearest Neighbor (KNN) algorithm to inspect and analyze the point cloud after preprocessing. On the basis of the design algorithm, four groups that contain measurement data for eight vehicle body parts are analyzed and the results prove the effectiveness of the algorithm, which is very suitable for the inspection process of vehicle body parts.

show abstract

“…The Iterative Closest Point (ICP) (Besl and McKay 1992) is often used, which offers several advantages. However, the input point clouds need to have a good initial registration, as the algorithm can become trapped in local minima (Attia and Slama 2017;Li et al 2020). To address this issue, several ICP variants have been developed over the years in order to enhance registration performance (e.g., Bae and Lichti 2008;Wujanz et al 2018;Kromer et al 2019;Li et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Register Multi-Platform Point Clouds for Rockfall Monitoring

Bolkas

Walton

Kromer

et al. 2021

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

View full text Add to dashboard Cite

Abstract. Point cloud produced from technologies such as terrestrial laser scanning (TLS) and photogrammetry (terrestrial and aerial) are widely used in rockfall monitoring applications due to the wealth of data they provide. In such applications, the acquisition and registration of multi-epoch point clouds is necessary. In addition, point clouds can be derived from different sensors (e.g., lasers versus digital cameras) and different platforms (terrestrial versus aerial). Therefore, registration methods should be able to support multi-platform datasets. Currently, registration of multi-platform datasets is done with manual intervention, and automatic registration is difficult. While registration of TLS point clouds can be achieved by targets that are not on the rock surface, this is not the case for photogrammetric methods, as ground control points (GCPs) should be located on the rock surface. Such GCPs can be lost or destroyed with time, and re-establishing them is difficult. Automated registration often relies on feature-based algorithms with refinement using the iterative closest point (ICP) algorithm. This paper presents a novel registration approach of multi-epoch and multi-platform point clouds to support rockfall monitoring applications. The registration method is based on edges that are detected in the different datasets using α-molecules. The paper shows application examples of the novel approach at different rock slopes in Colorado. Results demonstrate that the developed method in many cases performs better than the well-known ICP method and can be used to register point clouds and support rockfall monitoring.

show abstract

Robust point cloud registration based on topological graph and Cauchy weighted lq-norm

Cited by 33 publications

References 57 publications

Robust Estimation in Robot Vision and Photogrammetry: A New Model and Its Applications

Robust Estimation in Robot Vision and Photogrammetry: A New Model and Its Applications

An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data

A Novel Approach to Register Multi-Platform Point Clouds for Rockfall Monitoring

Contact Info

Product

Resources

About