A Framework for Position Uncertainty of Unorganised Three-Dimensional Point Clouds from Near-Monostatic Laser Scanners Using Covariance Analysis
MetadataShow full item record
Position uncertainty is one of the most important quantities of an unorganised three- dimensional point clouds since it provides the confidence level of any parametric estimation such as surface normal vector estimation and the registration of point clouds. We present an explicit form of position uncertainty based on the covariance analysis of a point. In addition, an explicit form of the variance of an estimated surface normal vector and an algorithm to evaluate an optimal size of the neighbourhood of a point which minimises the variance of the estimated normal vector are presented.
Showing items related by title, author, creator and subject.
Bae, Kwang-Ho (2006)Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different ...
Chan, T.; Lichti, D.; Belton, David; Hoang, L. (2016)Registration is an essential procedure for merging point clouds defined in different coordinate systems associated to different scanner positions and orientations. It is usually the first step before the point clouds are ...
Comparison of Observing Modes for Statistical Estimation of the 21 cm Signal from the Epoch of ReionisationTrott, Cathryn (2014)Noise considerations for experiments that aim to statistically estimate the 21 cm signal from high redshift neutral hydrogen during the Epoch of Reionisation (EoR) using interferometric data are typically computed assuming ...