Automatic scan registration using 3D linear and planar features

Yao, Jian; Ruggeri, Mauro R.; Taddei, Pierluigi; Vitor, Sequeira

doi:10.1007/3dres.03(2010)06

Cited by 28 publications

(28 citation statements)

References 26 publications

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“…For instance, Jaw and Chuang (2008) used linear and planar features to register TLS by using the different features individually and also by combining some of them. Yao et al (2010) also introduced an automatic registration method of laser scans using the extracted linear and planar features from the scans. In addition, photogrammetric data is incorporated to take advantage of additional information (Canaz and Habib, 2013).…”

Section: Introductionmentioning

confidence: 99%

RANSAC approach for automated registration of terrestrial laser scans using linear features

Al-Durgham

Habib

Kwak

2013

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

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ABSTRACT:The registration process of terrestrial laser scans (TLS) targets the problem of how to combine several laser scans in order to attain better information about features than what could be obtained through single scan. The main goal of the registration process is to estimate the parameters which determine geometrical variation between the origins of datasets collected from different locations. Scale, shifts, and rotation parameters are usually used to describe such variation. This paper presents a framework for the registration of overlapping terrestrial laser scans by establishing an automatic matching strategy that uses 3D linear features. More specifically, invariant separation characteristics between 3D linear features extracted from laser scans will be used to establish hypothesized conjugate linear features between the laser scans. These candidate matches are then used to geo-reference scans relative to a common reference frame. The registration workflow simulates the well-known RANndom Sample Consensus method (RANSAC) for determining the registration parameters, whereas the iterative closest projected point (ICPP) is utilized to determine the most probable solution of the transformation parameters from several solutions. The experimental results prove that the proposed methodology can be used for the automatic registration of terrestrial laser scans using linear features.

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Section: Introductionmentioning

confidence: 99%

RANSAC approach for automated registration of terrestrial laser scans using linear features

Al-Durgham

Habib

Kwak

2013

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

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“…The advantage of these approaches is the reduction of the search space identified by two small sets of features, which results in efficient matching, but that should account for extra computation time due to scene segmentation or feature detection. Among the feature types presented the most interesting are: lines [19,20], planes [19][20][21][22], circles [23], spheres [24] and other fitted geometric primitives [25].…”

Section: Related Workmentioning

confidence: 99%

“…As in [20], we present a variant of this RANSAC-based algorithm, which improves the random selection step by employing a weight function approximating the probability of each pair of features in P to be matched with one in Q. We call this variant as probabilitybased RANSAC approach and describe it in Section 4.1.…”

Section: Our Algorithmmentioning

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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“…Considering this, others have introduced matching methods by using rich descriptors for coarse registration (Rusu et al, 2009;Brusco et al, 2005;Diez et al, 2012;Yao et al, 2010). These methods are good for finding an initial alignment when a large misalignment is present, however, they are not suitable for fine registration.…”

Section: Methods 3: Point Matching By Normal Deviationmentioning

confidence: 99%

“…Zhang and Faugeras (1991) propose a method to find the transformation that minimizes the error between two line segment sets. Yao et al (2010) expand the registration by line segments to three-dimensional point clouds, using RANSAC (random sample consensus) (Fischler and Bolles, 1981). The extraction of robust geometrical primitives from an unstructured point cloud, such as those representing mining terrain, is a difficult task.…”

Section: Features In Point Cloud Datamentioning

confidence: 99%

Scan matching for terrain mapping in open-pit mining

Aguirre¹,

Antonio²

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This thesis is concerned with the problem of creating three-dimensional terrain maps using a highdensity, high-rate LiDAR sensor mounted on a moving platform without the use of an external localization solution. In particular, the thesis is motivated by the problem of building terrain maps in open-pit mining environments from data produced by haul-truck mounted LiDAR sensors.A foundation problem in map building from such data is the process of assembling together the point clouds generated by individual sensor scans into a common frame of reference. This falls into a broader class of well-studied problems called scan matching. Scan matching is most commonly solved by the Iterative Closet Point (ICP) method.This thesis makes two contributions. The first contribution is a comparison of the different ICP variants for the candidate application. The literature is replete with different algorithms that can be used at different stages of the ICP process. A population of 20,736 ICP-variants drawn from methods proposed in the literature is compared. Whilst others have looked to compare different ICP-variants, this investigation is distinguished from these earlier works in three respects: i) its comprehensiveness; ii) the focus of its target application (the loosely structured terrain of open-cut mining); and iii) its emphasis on how accuracy, precision, and computational efficiency trade-off across different variants. The main finding of this investigation is that the geometry of the point cloud critically determines the quality of the scan match. Significantly, of the variants considered, none were found to simultaneous meet requirements on accuracy, precision, and efficiency, highlighting the need for better approaches.The second contribution comes in the form of progress towards improving on the performance of established methods. For this, the thesis introduces the concept of "eigentropy" to quantify the geometric disorder or geometric information for points of a cloud. Eigentropy is conceptually similar to entropy as it appears in thermodynamics and information theory. Three novel methods are introduced to improve the performance of ICP-based scan-matching for loosely structured terrain. These methods are termed the: eigentropy filter, matching by normal deviation and unilateral eigentropy rejection, and they are based on the geometrical information content at a point. The results associated with these methods improve the overall performance of the ICP algorithm and five accurate, precise, efficient, and robust ICP variants are identified from an expanded population of 73,728. These five variants all use the methods introduced in this thesis.

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Automatic scan registration using 3D linear and planar features

Cited by 28 publications

References 26 publications

RANSAC approach for automated registration of terrestrial laser scans using linear features

RANSAC approach for automated registration of terrestrial laser scans using linear features

Robust surface registration using N-points approximate congruent sets

Scan matching for terrain mapping in open-pit mining

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