Book Description

Three-dimensional laser scanning has revolutionized spatial data acquisition and can be completed from a variety of platforms including airborne (ALS), mobile (MLS), and static terrestrial (TLS) laser scanning. MLS is a rapidly evolving technology that provides increases in efficiency and safety over static TLS, while still providing similar levels of accuracy and resolution. The componentry that make up a MLS system are more parallel to Airborne Laser Scanning (ALS) than to that of TLS. However, achievable accuracies, precisions, and resolution results are not clearly defined for MLS systems. As such, industry professionals need guidelines to standardize the process of data collection, processing, and reporting. This thesis lays the foundation for MLS guidelines with a thorough review of currently available literature that has been completed in order to demonstrate the capabilities and limitations of a generic MLS system. A key difference between MLS and TLS is that a mobile platform is able to collect a continuous path of geo-referenced points along the navigation path, while a TLS collects points from many separate reference frames as the scanner is moved from location to location. Each individual TLS setup must be registered (linked with a common coordinate system) to adjoining scan setups. A study was completed comparing common methods of TLS registration and geo-referencing (e.g., target, cloud-cloud, and hybrid methods) to assist a TLS surveyor in deciding the most appropriate method for their projects. Results provide insight into the level of accuracy (mm to cm level) that can be achieved using the various methods as well as the field collection and office processing time required to obtain a fully geo-referenced point cloud. Lastly, a quality assurance methodology has been developed for any form of LiDAR data to verify both the absolute and relative accuracy of a point cloud without the use of retro-reflective targets. This methodology incorporates total station validation of a scanners point cloud to compare slopes of common features. The comparison of 2D slope features across a complex geometry of cross-sections provides 3D positional error in both horizontal and vertical component. This methodology lowers the uncertainty of single point accuracy statistics for point clouds by utilizing a larger portion of a point cloud for statistical accuracy verification. This use of physical features for accuracy validation is particularly important for MLS systems because MLS systems cannot produce sufficient resolution on targets for accuracy validation unless they are placed close to the vehicle.