Non-parametric calibration for depth sensors

• Automatic calibration method for removing distortions of RGBD sensors.
• Non-parametric distortion model adequate for any depth sensor.
• The procedure is easy to apply and can be done by non-expert users.
• It does not require the use of any other software or of any calibration device.
• Freely available at http://easy_depth_calibration.dis.uniroma1.it.

RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstruction of the environment and mapping. In these tasks, uncalibrated sensors can generate poor quality results. In this paper we propose a quick and easy to use approach to estimate the undistortion function of RGBD sensors. Our approach does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute an extensive representation of the undistortion function as well as its statistics and use machine learning methods for approximation of the undistortion function. We validated our approach on datasets acquired from different kinds of RGBD sensors and using a precise 3D ground truth. We also provide a procedure for evaluating the quality of the calibration using a mobile robot and a 2D laser range finder. The results clearly show the advantages in using sensor data calibrated with the method described in this paper.