Robust range image registration using 3D lines

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

doi:10.1109/icip.2010.5652449

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Due to the near-certain presence of erroneous pairs in the preliminary matching A ini , none of optimization methods can be applied directly. Referring to [ 7 ], only K = 3 pairs of matched lines from A ini should be chosen to compute an initial transformation via the robust algorithm FMII 2 . The RANSAC algorithm randomly selects K data items, which are used to estimate the parameters.…”

Section: Methodsmentioning

confidence: 99%

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A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

Porêba

Goulette

2015

Sensors

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With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%.

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Section: Methodsmentioning

confidence: 99%

“…A wide variety of research has focused on automating the coarse registration and reduction of matching time. A current trend is two-step approach [ 6 , 7 ]. At first, a feature-based coarse registration provides an initial estimate, which is then passed to an ICP-like method for accurate and robust fine registration.…”

Section: Introductionmentioning

confidence: 99%

A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

Porêba

Goulette

2015

Sensors

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Automated Stitching of Microtubule Centerlines across Serial Electron Tomograms

et al. 2014

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Tracing microtubule centerlines in serial section electron tomography requires microtubules to be stitched across sections, that is lines from different sections need to be aligned, endpoints need to be matched at section boundaries to establish a correspondence between neighboring sections, and corresponding lines need to be connected across multiple sections. We present computational methods for these tasks: 1) An initial alignment is computed using a distance compatibility graph. 2) A fine alignment is then computed with a probabilistic variant of the iterative closest points algorithm, which we extended to handle the orientation of lines by introducing a periodic random variable to the probabilistic formulation. 3) Endpoint correspondence is established by formulating a matching problem in terms of a Markov random field and computing the best matching with belief propagation. Belief propagation is not generally guaranteed to converge to a minimum. We show how convergence can be achieved, nonetheless, with minimal manual input. In addition to stitching microtubule centerlines, the correspondence is also applied to transform and merge the electron tomograms. We applied the proposed methods to samples from the mitotic spindle in C. elegans, the meiotic spindle in X. laevis, and sub-pellicular microtubule arrays in T. brucei. The methods were able to stitch microtubules across section boundaries in good agreement with experts' opinions for the spindle samples. Results, however, were not satisfactory for the microtubule arrays. For certain experiments, such as an analysis of the spindle, the proposed methods can replace manual expert tracing and thus enable the analysis of microtubules over long distances with reasonable manual effort.

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Robust range image registration using 3D lines

Cited by 2 publications

References 9 publications

A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

Automated Stitching of Microtubule Centerlines across Serial Electron Tomograms

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