Method for Registering Lidar Data in Restrictive, Tunnel-Like Environments

• Main Author: Zacherl, Walter David
• Other Authors: Dereniak, Eustace
• Language: en_US
• Published: The University of Arizona. 2016
• Subjects: lidar; medical imaging; pattern recognition; registration; remote sensing; Optical Sciences; feature extraction
• Online Access: http://hdl.handle.net/10150/613145; http://arizona.openrepository.com/arizona/handle/10150/613145

A new method of registering multiple range datasets collected in a GPS-denied, tunnel-like environment is presented. The method is designed to function with minimal user inputs and be effective over a wide range of changes in observation angle. The method is initially developed to operate on data in a general 2.5D coordinate system. Then, the general registration method is specifically tailored to a 2.5D spherical coordinate system. To apply the method, the range data is first filtered with a series of discrete Gaussian-based filters to construct a second-order Taylor series approximation to the surface about each sampled point. Finally, principal curvatures are calculated and compared across neighboring datasets to determine homologies and the best fit transfer matrix. The new method relaxes the minimum change in perspective requirement between neighboring datasets typical of other algorithms. Results from the application of the method on both synthetic and real-world data are shown. The real-world data comes from a series of high explosive tests performed in a tunnel environment. The tunnels were oriented horizontally in rock and constructed with boring equipment. The tunnel surfaces were surveyed with a Faro Focus3D terrestrial panorama scanning light detection and ranging (lidar) system both before and after a high explosive device was detonated inside the tunnel with the intent of documenting damage to the tunnel surface.