Calibration of a Multi-Modal 3D Scanner

Collecting dense range measurements in uncontrolled environments is a challenging problem as lighting and surfaces' texture significantly influence the quality of the measurements. Instead of concentrating on improving a specific type of range sensors, the overall quality of the sensing can also be enhanced through the development of a mechanism that combines various range sensing technologies to form a multi-modal range sensor. Although many different multi-modal systems have been investigated, the problem of merging datasets have hinder engineers from producing unified data. Two major approaches have been used to rectify this problem: system calibration of the multi-modal system and data fitting of all datasets into a single model, which the latter is more widely used. The lack of multi-modal system calibration approaches is due to their complicated and lengthy nature, where individual calibration approaches must be applied to each subsystem and then applied between subsystems of the multi-modal range sensor. To alleviate the problems in multi-modal system calibration, straightforward and generic guidelines for calibration are defined and applied to an in-house multi-modal system built from a laser range finder system, two active triangulation systems using structured lighting, and a stereovision system. This paper addresses the system's intra- and inter-calibration processes and presents renderings of datasets collected with the calibrated multi-modal range sensor without the use of data fitting. From these results, the potential benefits of multi-modal calibration that reduces the need of data fitting and the advantages of merging subsystem's strengths to complement other subsystem's weaknesses are put in evidence