Exploiting the Manhattan-world assumption for extrinsic self-calibration of multi-modal sensor networks

Many new applications are enabled by combining a multi-camera system with a Time-of-Flight (ToF) camera, which is able to simultaneously record intensity and depth images. Classical approaches for self-calibration of a multi-camera system fail to calibrate such a system due to the very different image modalities. In addition, the typical environments of multi-camera systems are man-made and consist primary of only low textured objects. However, at the same time they satisfy the Manhattan-world assumption. We formulate the multi-modal sensor network calibration as a Maximum a Posteriori (MAP) problem and solve it by minimizing the corresponding energy function. First we estimate two separate 3D reconstructions of the environment: one using the pan-tilt unit mounted ToF camera and one using the multi-camera system. We exploit the Manhattan-world assumption and estimate multiple initial calibration hypotheses by registering the three dominant orientations of planes. These hypotheses are used as prior knowledge of a subsequent MAP estimation aiming to align edges that are parallel to these dominant directions. To our knowledge, this is the first self-calibration approach that is able to calibrate a ToF camera with a multi-camera system. Quantitative experiments on real data demonstrate the high accuracy of our approach.