Abstract
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.
Highlights
Measurements serve as the basic building blocks of maps and models, and as the relevance of indoor maps continues to grow, so will the demand for fast and affordable techniques for measuring indoor spaces
This paper presents a robust process for assessing indoor point cloud quality based on two measures: global accuracy at the level of a room and local out-of-plane accuracy and precision at the level of a flat surface
The measure of precision used here is the mean absolute deviation (MAD), which can be found by first calculating the mean out-of-plane value for each cell and finding the mean value of the absolute differences of all points in the cell
Summary
Measurements serve as the basic building blocks of maps and models, and as the relevance of indoor maps continues to grow, so will the demand for fast and affordable techniques for measuring indoor spaces. (a) Different datums, orientations, and units (b) Global versus (c) Mismatched local alignment local features All of these approaches present 3D measurements in the form of point clouds, or collections of xyz coordinate values that can number from thousands to millions of points. This paper presents a robust process for assessing indoor point cloud quality based on two measures: global accuracy at the level of a room and local out-of-plane accuracy and precision at the level of a flat surface. This process addresses the latter three of the four challenges mentioned earlier (unknown point correspondences with unmatched coordinate reference systems, geometric errors, and non-uniform point densities) and assumes the existence of a ground truth data set. The double-blind peer-review was conducted on the basis of the full paper
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