Exploiting Passthrough Information for Multi-view Object Reconstruction with Sparse and Noisy Laser Data

Martin Herrmann and Siddhartha Srinivasa
tech. report CMU-RI-TR-10-07, Robotics Institute, Carnegie Mellon University, February, 2010


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Abstract
We describe a probabilistic model for utilizing passthrough information for producing 3D geometry models from a rotating laser scanner. Our method is fast, performs particularly well with relatively sparse data, is robust to noise of the depth data and naturally handles grazing points. We demonstrate our results on the HERB platform where the robot automatically builds a watertight 3D model of an object it is handed.

Notes
Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Personal Robotics
Number of pages: 15

Text Reference
Martin Herrmann and Siddhartha Srinivasa, "Exploiting Passthrough Information for Multi-view Object Reconstruction with Sparse and Noisy Laser Data," tech. report CMU-RI-TR-10-07, Robotics Institute, Carnegie Mellon University, February, 2010

BibTeX Reference
@techreport{Herrmann_2010_6555,
   author = "Martin Herrmann and Siddhartha Srinivasa",
   title = "Exploiting Passthrough Information for Multi-view Object Reconstruction with Sparse and Noisy Laser Data",
   booktitle = "",
   institution = "Robotics Institute",
   month = "February",
   year = "2010",
   number= "CMU-RI-TR-10-07",
   address= "Pittsburgh, PA",
}