Abstract
The detection of markers or reflectors within point
cloud data (PCD) is often used for 3-D scan
registration, mapping, and 3-D environmental
modeling. However, the reliable detection of such
artifacts is diminished when PCD is sparse and
corrupted by detection and spatial errors, for
example, when the sensing environment is
contaminated by high dust levels, such as in
mines. In the radar literature, constant false alarm
rate (CFAR) processors provide solutions for
extracting features within noisy data; however,
their direct application to sparse, 3-D PCD is
limited due to the difficulty in defining a suitable
noise window. Therefore, in this article, CFAR
detectors are derived, which are capable of
processing a 2-D projected version of the 3-D PCD or
which can directly process the 3-D PCD
itself. Comparisons of their robustness, with
respect to data sparsity, are made with various
state-of-the-art feature detection methods, such as
the Canny edge detector and random sampling
consensus (RANSAC) shape detection methods.
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