An approach for collection of geospecific 3D features from terrestrial LIDAR

Abstract

Airborne LIDAR systems provide useful information from a top-down perspective; however, such sensors do not provide information about critical urban features (windows, doors, signs, etc) that are located below rooftops and under tree canopies. Vehicle-mounted terrestrial LIDAR sensors, on the other hand, provide the capability to capture highly accurate 3D measurements of the urban environment with spatial resolutions on the order of 5 centimeters or less. The 3D imaging capability of these collection systems is negated, however, by a lack of software tools and approaches capable of exploiting terrestrial LIDAR datasets with a high degree of automation. Current approaches for creating high-resolution 3D urban models are expensive, requiring for even a small scene thousands of man-hours to digitize feature geometries, assign textures to features, and then attribute features. In this paper we describe a new software system and architecture that provides the following benefits: (a) automated and fast extraction of complex urban features from large-volume terrestrial LIDAR datasets, (b) geospecific data extraction from terrestrial LIDAR and imagery including 3D geometry, texture, and attributes, (c) urban feature extraction workflows and vector data formats compatible with existing and planned simulation environment databases (d) workflow and software tool integration with existing commercial modeling and simulation software applications, and (e) data fusion of airborne and orbital imagery, GIS data, and other 2D and 3D mapping sources to constrain terrestrial LIDAR AFE approaches; thereby providing holistic support of modeling and simulation requirements for urban environments.