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Analysis and benchmarking of gravitational search algorithm against iterative closest point algorithm for point cloud registration

Having two cloud points P and Q, the point cloud registration aims to find a transformation (rotation and translation) to apply to P to match Q as good as possible. The iterative close point is a well-known algorithm in solving the point cloud registration problem. This iterative algorithm computes the mean, correspondence value, cross-covariance matrix, and single-valued decomposition to find the rotation and translation. This approach also can be easily adapted with an optimisation algorithm such as gravitational search algorithm. To solve the point cloud registration using gravitational search algorithm, error function such as mean square error can be used as objective function. If the cloud points have outliers, the objective function could be modified with a kernel function to deal with those outliers. During the first half of this project, the iterative close point algorithm is implemented as a benchmark to solve point cloud registration based on different cloud points of various level of difficulties. After that the cloud point registration is solved based on gravitational search algorithm. Finally, a statistical analysis is performed to analyse the performance of the two approaches.

Taidot: Algoritmi, C++ -ohjelmointi, C-ohjelmointi, Matlab ja Mathematica, Matematiikka

Näytä lisää: search result tag cloud php greasemonkey, display sharepoint search results tag cloud, analysis of binary search, analysis of binary search algorithm, binary search iterative, ros point cloud registration, gravitational search algorithm tutorial, gravitational search algorithm ppt, advantages of gravitational search algorithm, gravitational search algorithm matlab code, gravitational search algorithm wiki, gravitational search algorithm explained, gravitational search algorithm example, gravitational search algorithm source code, iterative closest point lidar, moodle cloud registration, point cloud registration python, point cloud registration matlab, real-time point cloud registration, point cloud registration github

Tietoa työnantajasta:
( 0 arvostelua ) Kuala Lumpur, Malaysia

Projektin tunnus: #32301323

5 freelanceria on tarjonnut keskimäärin $22 tähän työhön

assignsolver

Analysis and benchmarking of gravitational search algorithm against iterative closest point algorithm for point cloud registration I have read and understood all your project details and I feel my self the best candid Lisää

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3.9
IdealExpert

Analysis and benchmarking of gravitational search algorithm against iterative closest point algorithm for point cloud registration This project is my strength and I can fulfill your requirements properly within your g Lisää

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DanilVavilov

Hello, there I am an expert full stack development dealing with matlab, python, C++ etc... I have worked on many matlab projects. I am open to discuss more details of the project now, Looking to hearing from you. Plea Lisää

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Work12345x

Hi, I am a very talented software programmer with 13+ years of development experience (5+ years professional work experience). I am a results-oriented professional and possess experience using cutting-edge development Lisää

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anshkaush111

hello, i very glad to see ur this project . Im good in C++ programming . i have done so many project of this please give me one chance to perform with u Im sure u will be satisfy with me. thanks, have a good day.

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