Pattern- and Markerless RGB and Depth Sensor Calibration

With the help of Structure from Motion (SfM) algorithms the camera motion and a rough estimates of the scene geometry can be estimated from a video recording. Such approaches are an integral part of state-of-the-art commercial software. Recently, affordable depth sensors have become available on the consumer market, which estimate the spatial depth for each pixel. There have already been approaches to jointly calibrate RGB and depth sensors, but they require special patterns or markers. The goal of this bachelor thesis is to develop an approach that calibrates RGB and depth sensors without the help of a marker or pattern.

In order to achieve this goal, the RGB video recordings are calibrated using off-the-shelf software such as Nuke. Additionally, a quasi dense depth image is computed from the individual frames. Afterwards the point cloud reconstructed from the data of the depth sensor can be registered to the depth values with the Iterative Closest Point (ICP) algorithm.