• Corpus ID: 54967602

A Survey of Rigid 3D Pointcloud Registration Algorithms

  title={A Survey of Rigid 3D Pointcloud Registration Algorithms},
  author={Ben Bellekens and Vincent Spruyt and Rafael Berkvens and Maarten Weyn},
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
Advanced user interface sensors are able to observe the environment in three dimensions with the use of specific optical techniques such as time-of-flight, structured light or stereo vision. Due to
3D Point Cloud Registration using A-KAZE Features and Graph Optimization
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
This paper addresses the problem of registering multiple point sets by building upon the state-of-the-art Joint Registration of Multiple Point Clouds (JRMPC) algorithm by incorporating the surface normal orientation of each point in the Gaussian Mixture Models (GMM) employed by this method.
3D LiDAR-based point cloud map registration: Using spatial location of visual features
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.
A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges
This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration to help readers quickly get into the problems of their interests related to point could registration.
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
Plane Pair Matching for Efficient 3D View Registration
A novel method to estimate the motion matrix between overlapping pairs of 3D views in the context of indoor scenes using the Manhattan world assumption and a stochastic framework to categorize planes as vertical or horizontal and parallel or non-parallel is presented.
Robust object localization based on error patterns learning for dexterous mobile manipulation
Experiments show that exploiting the known information about the spatial properties of the objects, together with appropriate pre-processing and refining of the data, can have a substantial improvement in discarding wrong hypothesis for geometrically ambiguous items.
CICP: Cluster Iterative Closest Point for sparse-dense point cloud registration
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
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.


Tracking a depth camera: Parameter exploration for fast ICP
This paper proposes a state-of-the-art, modular, and efficient implementation of an ICP library, and shows the modularity of this library by optimizing the use of lean and simple descriptors in order to ease the matching of 3D point clouds.
An evaluation of the RGB-D SLAM system
We present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the trajectory of a hand-held Kinect and
Dense visual SLAM for RGB-D cameras
This paper proposes a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels, and proposes an entropy-based similarity measure for keyframe selection and loop closure detection.
Method for registration of 3-D shapes
This paper describes a general purpose, representation independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method
KinectFusion: Real-time dense surface mapping and tracking
We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware.
An Explicit Loop Closing Technique for 6D SLAM
A novel approach for solving SLAM using 3D laser range scans using a novel explicit loop closing heuristic (ELCH), which dissociates the last scan of a sequence of acquired scans, reassociates it to the map, built so far by scan registration, and distributes the difference in the pose error over the SLAM graph.
3D generic object categorization, localization and pose estimation
  • S. Savarese, Li Fei-Fei
  • Computer Science, Mathematics
    2007 IEEE 11th International Conference on Computer Vision
  • 2007
This work proposes a novel and robust model to represent and learn generic 3D object categories, and proposes a framework in which learning is done via minimal supervision compared to previous works.
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera
A system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight, which enables 3D flight in cluttered environments using only onboard sensor data.
Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration
The Iterative Closest Point (ICP) algorithm that uses the point-toplane error metric has been shown to converge much faster than one that uses the point-to-point error metric. At each iteration of
In this paper we combine the Iterative Closest Point (’ICP’) and ‘point-to-plane ICP‘ algorithms into a single probabilistic framework. We then use this framework to model locally planar surface