Automatic graph based spatiotemporal extrinsic calibration of multiple Kinect V2 ToF cameras

This paper presents a novel technique for the automatic calibration of motion capture systems composed of multiple Time of Flight cameras (ToF) or any equivalent device able to provide 3D point clouds together with chromatic information. The calibration procedure is designed to be simple, fast and unsupervised: a colored sphere moved freely by hand is used as calibration tool. Three elements in particular distinguish the method we propose from other state of the art solutions: it takes into account and propagates measurement uncertainties, it allows the identification and recovery of systematic time delays in camera synchronization, it optimize the extrinsic parameters by means of a graph based approach. A widely used ToF, Microsoft Kinect V2, was used for the comparison with state of the art multi-ToF calibration techniques. Experimental results demonstrated that the proposed method is the most accurate both in terms of shape reconstruction and spatial consistency. Furthermore, the experimental results underlined that the application of the global graph optimization over a more standard couple-based unweighed matching achieves a higher accuracy in the assessment of the extrinsic parameters. Thus enabling the reduction of both systematic errors and data dispersion in 3D point clouds of reference shapes. The comparison among acquisition systems composed of 3 and 6 devices underlined that graph optimization becomes relevant when the number of devices grows. Taking into account several 3D configurations calibrated with the proposed method, it was assessed that the uncertainty of the extrinsic parameters is generally lower than 2 mm and 10-2 radiants, 0.6. The resulting dense 3D reconstruction of objects, both static and in motion, achieved experimental errors lower than to 10 mm, approximately half of other state of the art methods, and, in first approximation, homogeneous for the entire monitored area. The software developed can be found at the site of the MIRo lab (Measurement Instrumentation and Robotics lab) at link(1) under the Creative Commons Attribution conditions. (C) 2017 Elsevier B.V. All rights reserved.