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 Azure Kinect cameras, based on 2-D checkerboard corner detection. This method allows independent calibration of each camera without requiring a main-secondary configuration. Calibration was tested with two- and four-camera setups, positioned at heights of 1.2 and 2 m, with varying distances (1–2.29 m) and orientations relative to the checkerboard. Results showed high accuracy, with mean calibration errors (CEs) 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 multicamera 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.