Model building for simulation and testing under uncertain conditions

Abstract

D models of real world environments are becoming increasingly important for a variety of applications: Vehicle simulators can be enhanced through accurate models of real world terrain and objects; Robotic security systems can benefit from as-built layout of the facilities they patrol; Vehicle dynamics modeling and terrain impact simulation can be improved through validation models generated by digitizing real tire/soil interactions. Recently, mobile scanning systems have been developed that allow 3D scanning systems to undergo the full range of motion necessary to acquire such real-world data in a fast, efficient manner. As with any digitization system, these mobile scanning systems have systemic errors that adversely affect the 3D models they are attempting to digitize. In addition to the errors given by the individual sensors, these systems also have uncertainties associated with the fusion of the data from several instruments. Thus, one of the primary foci for 3D model building is to perform the data fusion and post-processing of the models in such a manner as to reconstruct the 3D geometry of the scanned surfaces as accurately as possible, while alleviating the uncertainties posed by the acquisition system. We have developed a modular scanning system that can be configured for a variety of application resolutions, as well as the algorithms necessary to fuse and process the acquired data. This paper presents the acquisition system and the tools utilized for constructing 3D models under uncertain real-world conditions, as well as some experimental results on both synthetic and real 3D data.