Range image registration for industrial inspection

Matabosch, Carles; Salví, Joaquím; Fofi, David; Mériaudeau, Fabrice

doi:10.1117/12.585244

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…According to Habib and Al-Ruzouq [11], a comprehensive registration process should address four issues: (1) the transformation parameters relating the involved datasets to a common reference frame, (2) the registration primitives used for the estimation of the transformation parameters, (3) the mathematical constraint describing the similarity metric between conjugate features after registration, and (4) the matching strategy controlling the framework for the automatic registration process. Depending on the final accuracy of the estimated transformation parameters, existing procedures can be categorized as either coarse or fine registration techniques [12]. Coarse registration is commonly used to establish rough alignment between the involved point clouds.…”

Section: Introductionmentioning

confidence: 99%

Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds

Al‐Rawabdeh

Habib

2020

Remote Sensing

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The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration procedures are classified as either coarse or fine registration. Coarse registration is typically used to establish a rough alignment between the involved point clouds. Fine registration starts from coarsely aligned point clouds to achieve more precise alignment of the involved datasets. In practice, the acquired/derived point clouds from laser scanning and image-based dense matching techniques usually include an excessive number of points. Fine registration of huge datasets is time-consuming and sometimes difficult to accomplish in a reasonable timeframe. To address this challenge, this paper introduces two down-sampling approaches, which aim to improve the efficiency and accuracy of the iterative closest patch (ICPatch)-based fine registration. The first approach is based on a planar-based adaptive down-sampling strategy to remove redundant points in areas with high point density while keeping the points in lower density regions. The second approach starts with the derivation of the surface normals for the constituents of a given point cloud using their local neighborhoods, which are then represented on a Gaussian sphere. Down-sampling is ultimately achieved by removing the points from the detected peaks in the Gaussian sphere. Experiments were conducted using both simulated and real datasets to verify the feasibility of the proposed down-sampling approaches for providing reliable transformation parameters. Derived experimental results have demonstrated that for most of the registration cases, in which the points are obtained from various mapping platforms (e.g., mobile/static laser scanner or aerial photogrammetry), the first proposed down-sampling approach (i.e., adaptive down-sampling approach) was capable of exceeding the performance of the traditional approaches, which utilize either the original or randomly down-sampled points, in terms of providing smaller Root Mean Square Errors (RMSE) values and a faster convergence rate. However, for some challenging cases, in which the acquired point cloud only has limited geometric constraints, the Gaussian sphere-based approach was capable of providing superior performance as it preserves some critical points for the accurate estimation of the transformation parameters relating the involved point clouds.

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Section: Introductionmentioning

confidence: 99%

Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds

Al‐Rawabdeh

Habib

2020

Remote Sensing

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“…Recently, several local registration methods with a higher accuracy have been proposed. Some studies [ 13 , 14 ] provide an overview of the fine and coarse registration methods. According to Reference [ 14 ], there are four types of local registration methods: the ICP method, Chen’s method [ 15 , 16 ], signed distance fields [ 17 ], and genetic algorithms [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Indoor Scene Point Cloud Registration Algorithm Based on RGB-D Camera Calibration

Tsai

2017

Sensors

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With the increasing popularity of RGB-depth (RGB-D) sensor, research on the use of RGB-D sensors to reconstruct three-dimensional (3D) indoor scenes has gained more and more attention. In this paper, an automatic point cloud registration algorithm is proposed to efficiently handle the task of 3D indoor scene reconstruction using pan-tilt platforms on a fixed position. The proposed algorithm aims to align multiple point clouds using extrinsic parameters of the RGB-D camera obtained from every preset pan-tilt control point. A computationally efficient global registration method is proposed based on transformation matrices formed by the offline calibrated extrinsic parameters. Then, a local registration method, which is an optional operation in the proposed algorithm, is employed to refine the preliminary alignment result. Experimental results validate the quality and computational efficiency of the proposed point cloud alignment algorithm by comparing it with two state-of-the-art methods.

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“…The problem of automatically registering two scans was achieved with a wide variety of methods [7]. Most of these extract sets of feature points, which are automatically matched to recover a good approximation of T .…”

Section: Related Workmentioning

confidence: 99%

Robust surface registration using N-points approximate congruent sets

Yao

Ruggeri

Taddei

et al. 2011

EURASIP J. Adv. Signal Process.

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Scans acquired by 3D sensors are typically represented in a local coordinate system. When multiple scans, taken from different locations, represent the same scene these must be registered to a common reference frame. We propose a fast and robust registration approach to automatically align two scans by finding two sets of N-points, that are approximately congruent under rigid transformation and leading to a good estimate of the transformation between their corresponding point clouds. Given two scans, our algorithm randomly searches for the best sets of congruent groups of points using a RANSAC-based approach. To successfully and reliably align two scans when there is only a small overlap, we improve the basic RANSAC random selection step by employing a weight function that approximates the probability of each pair of points in one scan to match one pair in the other. The search time to find pairs of congruent sets of N-points is greatly reduced by employing a fast search codebook based on both binary and multi-dimensional lookup tables. Moreover, we introduce a novel indicator of the overlapping region quality which is used to verify the estimated rigid transformation and to improve the alignment robustness. Our framework is general enough to incorporate and efficiently combine different point descriptors derived from geometric and texture-based feature points or scene geometrical characteristics. We also present a method to improve the matching effectiveness of texture feature descriptors by extracting them from an atlas of rectified images recovered from the scan reflectance image. Our algorithm is robust with respect to different sampling densities and also resilient to noise and outliers. We demonstrate its robustness and efficiency on several challenging scan datasets with varying degree of noise, outliers, extent of overlap, acquired from indoor and outdoor scenarios.

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Range image registration for industrial inspection

Cited by 18 publications

References 17 publications

Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds

Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds

Indoor Scene Point Cloud Registration Algorithm Based on RGB-D Camera Calibration

Robust surface registration using N-points approximate congruent sets

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