Easy to Calibrate: Marker-Less Calibration of Multiview Azure Kinect

Abstract

Reconstructing a three-dimensional (3D) environment is an indispensable technique to make augmented reality and augmented virtuality feasible. A Kinect device is an efficient tool for reconstructing 3D environments, and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces. To employ multiple devices simultaneously, Kinect devices need to be calibrated with respect to each other. There are several schemes available that calibrate 3D images generated from multiple Kinect devices, including the marker detection method. In this study, we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection, which directly affects calibration accuracy; it offers superior user-friendliness, efficiency, and accuracy. Further, we applied a joint tracking algorithm to approximate the calibration. Traditional methods require the information of multiple joints for calibration; however, Azure Kinect, the latest version of Kinect, requires the information of only one joint. The obtained result was further refined using the iterative closest point algorithm. We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional marker-based calibration.