Multi-modal sensory data plays an important role in many computer vision and robotics tasks. One popular multi-modal pair is cameras and laser scanners. To overlay and jointly use the data from both modalities, it is necessary to calibrate the sensors, i.e., to obtain the spatial relation between the sensors. Computing such a calibration is challenging as both sensors provide quite different data: cameras yield color or brightness information, laser scanners yield 3-D points. However, several laser scanners additionally provide reflectances, which turn out to make calibration to a camera well feasible. To this end, we first estimate a rough alignment of the coordinate systems of both modalities. Then, we use the laser scanner reflectances to compute a virtual image of the scene. Stereo calibration on the virtual image and the camera image are then used to compute a refined, high-accuracy calibration. It is encouraging that the accuracies in our experiments are comparable to camera-camera stereo setups and outperform another of other target-based calibration approach. This shows that the proposed algorithm reliably integrates the point cloud with the intensity image. As an example application, we use the calibration results to obtain ground-truth distance images for range cameras. Furthermore, we utilize this data to investigate the accuracy of the Microsoft Kinect V2 time-of-flight and the Intel RealSense R200 structured light camera.

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