Calibration of an integrated robotic multimodal range scanner

Collecting dense range measurements in uncontrolled environments is a challenging problem, as the quality of the measurements is highly dependent on the lighting conditions and the texture of the target surfaces. This dependence affects the registration and data-fusion processes and, consequently, degrades the accuracy of the surface or occupancy models that are computed from the range measurements. Typical approaches to address this issue have concentrated on improving a specific type of range sensor. On the other hand, the overall quality of the sensing can also be enhanced through the development of a mechanism that combines the various range-sensing technologies to form a multimodal range sensor. The resulting problem of the merging datasets can then be solved in two ways: system calibration of the multimodal sensor or data fitting of all the datasets into a single model, of which the latter is more widely implemented. The lack of multimodal-system calibration approaches is due to their complicated and lengthy nature, where individual calibration procedures must be applied to each subsystem and then applied between the subsystems of the multimodal range sensor. This paper proposes a technique to alleviate the problems encountered in a multimodal-system calibration. Straightforward and generic guidelines for the calibration are defined and applied to an in-house integrated multimodal system built from a laser-range-finder system, two structured-lighting systems, and a stereovision system. The system's intracalibration and intercalibration processes are detailed. Reconstructed renderings of the datasets collected with the calibrated multimodal range sensor, without the use of data fitting, are also presented. From these results, the potential benefits of multimodal calibration over the computationally intensive data-fitting methods and the advantages of merging the subsystem's strengths to complement other subsystem's weaknesses are put in evidence