A self-calibration approach for multi-view RGB-D sensing

In this paper, an approach for the estimation of relative positions and orientations of multiple rigidly coupled RGB-D sensors is presented. The proposed methodology does not impose constraints on the relative pose among the cameras, except for the rigidity: relative poses among cameras do not change in time. We assume that each camera captures an individual sequence which is afterward processed by a Simultaneous Localization And Mapping (SLAM) algorithm resulting in positions and orientations describing the trajectory followed by each sensor. Several simulations have been run considering a two-camera set having known input trajectories, affected by increasing levels of white noise. The results prove the capability of the algorithm in estimating the relative translations and rotations between the two cameras. Furthermore, experimental laboratory tests corroborate the effectiveness of the proposed method.