Real-Time Scale Invariant 3D Range Point Cloud Registration

  • Anuj Sehgal
  • Daniel Cernea
  • Milena Makaveeva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6111)


Stereo cameras, laser rangers and other time-of-flight ranging devices are utilized with increasing frequency as they can provide information in the 3D plane. The ability to perform real-time registration of the 3D point clouds obtained from these sensors is important in many applications. However, the tasks of locating accurate and dependable correspondences between point clouds and registration can be quite slow. Furthermore, any algorithm must be robust against artifacts in 3D range data as sensor motion, reflection and refraction are commonplace. The SIFT feature detector is a robust algorithm used to locate features, but cannot be extended directly to the 3D range point clouds since it requires dense pixel information, whereas the range voxels are sparsely distributed. This paper proposes an approach which enables SIFT application to locate scale and rotation invariant features in 3D point clouds. The algorithm then utilizes the known point correspondence registration algorithm in order to achieve real-time registration of 3D point clouds.


Point Cloud Scale Invariant Feature Transform Iterative Close Point Algorithm Scale Invariant Feature Transform Feature RANSAC Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anuj Sehgal
    • 1
  • Daniel Cernea
    • 2
  • Milena Makaveeva
    • 3
  1. 1.Indian Underwater Robotics SocietyNoidaIndia
  2. 2.Department of Computer ScienceUniversity of KaiserslauternKaiserslauternGermany
  3. 3.Computer ScienceJacobs University BremenBremenGermany

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