Volume IV-4/W6 | Copyright
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W6, 3-10, 2018
https://doi.org/10.5194/isprs-annals-IV-4-W6-3-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018

ASSESSING THE ACCURACY AND PRECISION OF IMPERFECT POINT CLOUDS FOR 3D INDOOR MAPPING AND MODELING

J. Chen1, O. E. Mora2, and K. C. Clarke3 J. Chen et al.
  • 1Department of Geography, University of California, Santa Barbara, USA
  • 2Department of Civil Engineering, California State Polytechnic University, Pomona, USA
  • 3Department of Geography, University of California, Santa Barbara, USA

Keywords: point clouds, remote sensing, indoor mapping

Abstract. In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner.

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