Genetic algorithms for free-form surface matching

Brunnström, Kjell; Stoddart, Andrew J.

doi:10.1109/icpr.1996.547653

Cited by 80 publications

(55 citation statements)

References 7 publications

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“…Matching methods are based on the process of matching points from both surfaces, as Ransac or Genetic Algorithm. In some situations both techniques can be combined to find correspondences, as Brunnström [13], who used the normal vectors at every point to define the fitness function of the genetic algorithm. On the other hand, techniques of both groups can be used independently as Ransac which do not use features in the matching process or Point Signature that when points are characterized only a comparison between features from both surfaces is required to detect correspondences.…”

Section: Coarse Registration Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A review of recent range image registration methods with accuracy evaluation

Salví

Matabosch

Fofi

et al. 2007

Image and Vision Computing

555

314

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The three-dimensional reconstruction of real objects is an important topic in computer vision. Most of the acquisition systems are limited to reconstruct a partial view of the object obtaining in blind areas and occlusions, while in most applications a full reconstruction is required. Many authors have proposed techniques to fuse 3D surfaces by determining the motion between the different views. The first problem is related to obtaining a rough registration when such motion is not available. The second one is focused on obtaining a fine registration from an initial approximation. In this paper, a survey of the most common techniques is presented. Furthermore, a sample of the techniques has been programmed and experimental results are reported to determine the best method in the presence of noise and outliers, providing a useful guide for an interested reader including a Matlab toolbox available at the webpage of the authors.

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Section: Coarse Registration Methodsmentioning

confidence: 99%

“…Brunnströ m and Stoddart [13] used a genetic algorithm to solve the problem of searching for correspondences between two range images. The interest in this method is centered on defining the vector that contains the n index of correspondences between both range images, where the size of the vector is set to n, i.e.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A review of recent range image registration methods with accuracy evaluation

Salví

Matabosch

Fofi

et al. 2007

Image and Vision Computing

555

314

View full text Add to dashboard Cite

show abstract

“…These later methods -like ICP [14] -are typically slow and show bad convergence properties, requiring an initial step for object detection. Methods for coarse registration include PCA, local feature based approaches like harmonic shape contexts [15], spin images [16], RANSAC-based approaches like DARCES [17] and genetic algorithms [18]. A drawback with many of the popular algorithms (like spin images) is that they require the presence of local features, i.e.…”

Section: Introductionmentioning

confidence: 99%

A robotic concept for remote maintenance operations: A robust 3D object detection and pose estimation method and a novel robot tool

Transeth

Skotheim

Schumann-Olsen

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Future normally-unmanned oil platforms offer potentially significantly lower commissioning and operation costs than their current manned counterparts. The ability to initiate and perform remote inspection and maintenance (I&M) operations is crucial for maintaining such platforms. This paper presents a system solution, including key components such as a 3D robot vision system, a robot tool and a control architecture for remote I&M operations on processes similar to those on topside oil platforms. In particular, a case study on how to automatically replace a battery in a wireless process sensor is investigated. A novel robot tool for removing and re-attaching the sensor lid has been designed. Moreover, a robot control architecture for remote control of industrial-type robot manipulators is presented. A 3D robot vision system for localizing the sensor lid and the battery has been developed. The system utilizes structured light, using an off-the-shelf projector and a standard machine vision camera. A novel, robust and fast vision algorithm called 3D-MaMa has been adapted to work for object localization and pose estimation in complex scenes, in our case the process equipment in our lab facility. Experimental results from our lab facility are presented which describe a series of battery replacement operations for various unknown positions of the wireless sensor, and we report on accuracies and success ratios. The experiments demonstrate that the described vision system is able to recover the full pose and orientation of an object, and that the results are directly applicable for controlling advanced robot contact operations. Moreover, the custom-built lid operation tool demonstrates successful results.

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“…ordinary Least Squares adjustment, Genetic Algorithms (Brunnström & Stoddart, 1996), Principal Component Analysis ''PCA'' (Chung & Lee, 1998) or RANdom Sample And Consensus ''RANSAC'' (Fischler & Bolles, 1981)). Kang et al (2009) proposed an iterative algorithm, using Lease Squares adjustment, for optimal transformation parameters estimation.…”

Section: Introductionmentioning

confidence: 99%

Speeding Up Coarse Point Cloud Registration by Threshold-Independent Baysac Match Selection

Kang¹,

Lindenbergh²,

Pu³

2016

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

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ABSTRACT:This paper presents an algorithm for the automatic registration of terrestrial point clouds by match selection using an efficiently conditional sampling method --threshold-independent BaySAC (BAYes SAmpling Consensus) and employs the error metric of average point-to-surface residual to reduce the random measurement error and then approach the real registration error. BaySAC and other basic sampling algorithms usually need to artificially determine a threshold by which inlier points are identified, which leads to a threshold-dependent verification process. Therefore, we applied the LMedS method to construct the cost function that is used to determine the optimum model to reduce the influence of human factors and improve the robustness of the model estimate. Point-topoint and point-to-surface error metrics are most commonly used. However, point-to-point error in general consists of at least two components, random measurement error and systematic error as a result of a remaining error in the found rigid body transformation. Thus we employ the measure of the average point-to-surface residual to evaluate the registration accuracy. The proposed approaches, together with a traditional RANSAC approach, are tested on four data sets acquired by three different scanners in terms of their computational efficiency and quality of the final registration. The registration results show the st.dev of the average point-to-surface residuals is reduced from 1.4 cm (plain RANSAC) to 0.5 cm (threshold-independent BaySAC). The results also show that, compared to the performance of RANSAC, our BaySAC strategies lead to less iterations and cheaper computational cost when the hypothesis set is contaminated with more outliers.

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Genetic algorithms for free-form surface matching

Cited by 80 publications

References 7 publications

A review of recent range image registration methods with accuracy evaluation

A review of recent range image registration methods with accuracy evaluation

A robotic concept for remote maintenance operations: A robust 3D object detection and pose estimation method and a novel robot tool

Speeding Up Coarse Point Cloud Registration by Threshold-Independent Baysac Match Selection

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