Multiview Marker-Free Free Registration of Forest Terrestrial Laser Scanner Data With Embedded Confidence Metrics
Abstract: Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration ation techniques (based on retro retro-reflective reflective spherical targets) are commonly used in practice, a blind marker marker-free free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registrati registration on approach of our earlier work, and develop a graph-theoretical theoretical framework to perform blind marker marker-free free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose co conflict nflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, etric, with a mean estimated root root-mean-square square error of 7.2 cm and 89% of scans connected to the reference node. This paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registra registration algorithm.