Accurate Extrinsic Calibration of multiple Azure Kinect using a planar checkerboard

Initially developed for entertainment, the Kinect, has become widely used in robotics, rehabilitation, and human-computer interaction thanks to its RGB-D image capture and body tracking capabilities. However, professional applications reveal limitations such as quantization noise, low resolution, and occlusion, which can compromise accuracy. A potential solution is the use of a properly calibrated Kinect network to ensure consistent output across cameras. This study proposes a flexible extrinsic calibration procedure for Kinect Azure cameras, based on 2D checkerboard corner detection. This method allows independent calibration of each camera without requiring a master-slave configuration. Calibration was tested with two- and four-camera setups, positioned at heights of 1.2 m and 2 m, with varying distances (1–2.29 m) and orientations relative to the checkerboard. Results showed high accuracy, with mean calibration errors (CE) below 1 cm under optimal conditions, reaching a minimum of 3.3 mm when the checkerboard was placed 1.75 m from the cameras. The proposed method proved effective in multi-camera setups, providing large calibration volumes. This study highlights the feasibility and flexibility of the proposed approach, with potential for future research with different sensors and varying conditions.