• Corpus ID: 54967602

A Survey of Rigid 3D Pointcloud Registration Algorithms

@inproceedings{Bellekens2014ASO,
  title={A Survey of Rigid 3D Pointcloud Registration Algorithms},
  author={Ben Bellekens and Vincent Spruyt and Rafael Berkvens and Maarten Weyn},
  year={2014}
}
Geometric alignment of 3D pointclouds, obtained using a depth sensor such as a time-of-flight camera, is a challenging task with important applications in robotics and computer vision. Due to the recent advent of cheap depth sensing devices, many different 3D registration algorithms have been proposed in literature, focussing on different domains such as localization and mapping or image registration. In this survey paper, we review the state-of-the-art registration algorithms and discuss their… 
A Benchmark Survey of Rigid 3D Point Cloud Registration Algorithms
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TLDR
The main idea of this paper is to compute 2D A-KAZE feature correspondence and map to 3D for obtaining more reliable 3D sparse points and shows that the algorithm is able to estimate the accurate pose with least alignment error as compared with ICP and SVD methods.
Joint Registration of Multiple Point Sets with Refinement
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3D LiDAR-based point cloud map registration: Using spatial location of visual features
TLDR
An improved 3D point cloud map registration method is proposed to register precisely and effectively two maps using low-cost cameras and the effectiveness of the presented method is quantitatively validated by experiment on challenging instances of the merging problem and comparison with an existing registration method.
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Registration of Point Clouds based on Global Super-Point Features using Auto-Encoder Deep Neural Network
Registration of scanned point clouds is the process of integrating two separate local point clouds into one global coordinate system. This process is a key stage in robotic vision SLAM[1], [2], 3D
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CICP: Cluster Iterative Closest Point for sparse-dense point cloud registration
TLDR
A novel approach that surpasses the notion of density is proposed, which consists in matching points representing each local surface of source cloud with the points representing the corresponding local surfaces in the target cloud.
A Fast GPU Point-cloud Registration Algorithm
  • M. Rahman, P. Galanakou, G. Kalantzis
  • Computer Science
    2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)
  • 2018
TLDR
A standard Singular Value Decomposition (SVD) and a truncated SVD (TSVD) point cloud registration algorithm that indicates that for the full registration process GTX1080Ti indicated a linear increase of speedup factors versus the number of pixels, fact that renders it is the most suitable GPU card with respect to the other GPU cards used for the specific application.
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