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.