This paper analyzes the accuracy and resolution of Kinect depth data for indoor mapping applications. The Kinect sensor captures depth and color images simultaneously at 30 fps, producing a colored point cloud with about 300,000 points per frame. The depth data is derived from disparity measurements using a mathematical model, and the accuracy and resolution of the data are analyzed. The sensor's depth data is affected by factors such as distance from the sensor, object surface properties, and measurement setup. The random error in depth measurement increases with distance, ranging from a few millimeters to about 4 cm at the maximum range of 5 meters. The depth resolution also decreases with distance, with point spacing in the depth direction reaching up to 7 cm at 5 meters. The paper presents a theoretical random error model and verifies it through experiments. Calibration of the Kinect sensor is essential for accurate depth and color data alignment. The results show that the Kinect depth data has lower accuracy and resolution compared to high-end laser scanners, but it can still be used for indoor mapping applications within a 1–3 meter distance from the sensor. The paper concludes that proper calibration is necessary to minimize systematic errors and improve the accuracy of Kinect depth data for mapping applications.This paper analyzes the accuracy and resolution of Kinect depth data for indoor mapping applications. The Kinect sensor captures depth and color images simultaneously at 30 fps, producing a colored point cloud with about 300,000 points per frame. The depth data is derived from disparity measurements using a mathematical model, and the accuracy and resolution of the data are analyzed. The sensor's depth data is affected by factors such as distance from the sensor, object surface properties, and measurement setup. The random error in depth measurement increases with distance, ranging from a few millimeters to about 4 cm at the maximum range of 5 meters. The depth resolution also decreases with distance, with point spacing in the depth direction reaching up to 7 cm at 5 meters. The paper presents a theoretical random error model and verifies it through experiments. Calibration of the Kinect sensor is essential for accurate depth and color data alignment. The results show that the Kinect depth data has lower accuracy and resolution compared to high-end laser scanners, but it can still be used for indoor mapping applications within a 1–3 meter distance from the sensor. The paper concludes that proper calibration is necessary to minimize systematic errors and improve the accuracy of Kinect depth data for mapping applications.