RGB-Depth (or RGB-D) cameras are increasingly being adopted for real-world applications, especially for applications in the areas of healthcare and at-home monitoring. As for any other sensor, and since the manufacturer's parameters (e.g., focal length) might change between models, calibration is necessary to increase the camera's sensing accuracy. In this paper, we present a novel RGB-D camera-calibration algorithm that is easy to use even for non-expert users at their home; our method can be used for any arrangement of RGB and depth sensors, and only requires that a spherical object (e.g., a basketball) is moved in front of the camera for a few seconds. A robust image-processing pipeline automatically detects the moving sphere and rejects noise and outliers in the image data. A novel closed-form solution is presented to accurately compute an initial set of calibration parameters which are then utilized in a nonlinear minimization stage over all the camera parameters including lens distortion. Extensive simulation and experimental results show the accuracy and robustness to outliers of our algorithm with respect to existing checkerboard-based methods. Furthermore, an RGB-D Calibration Toolbox for MATLAB is made freely available for the entire research community.