Accuracy and resolution of Kinect depth data for indoor mapping applications

Abstract

Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

Keywords: RGB-D; calibration; error budget; imaging; laser scanning; point cloud; range camera; sensor; triangulation.

Figure 1.

(a) Infrared image of the pattern of speckles projected on a sample scene. (b) The resulting depth image.

Figure 2.

Relation between relative depth and measured disparity.

Figure 3.

Lens distortions of (a) IR camera and (b) RGB camera. The principal points are marked by x and the image centers by +.

Figure 4.

Radial distortion curves for the IR and RGB images.

Figure 5.

Linear relation of normalized disparity with inverse depth.

Figure 6.

Comparison of Kinect point cloud (cyan) with the point cloud obtained by FARO LS880 laser scanner (white). The larger points are samples randomly selected from the Kinect data (blue) and their closest point in the laser scanner data (red). The thumbnail on the lower right is a color image of the setup.

Figure 7.

Histograms of discrepancies between the closest point pairs in X, Y and Z direction.

Figure 8.

Distribution of point pair distances in the X-Z plane.

Figure 9.

The planar surface of a door measured at different distances to the sensor. The boxes show the plane fitting area.

Figure 10.

Standard deviation of plane fitting residuals at different distances of the plane to the sensor. The curves show the theoretical random error (red) and depth resolution (blue).

Figure 11.

Point cloud of a planar surface at 1 meter (a), 3 meter (b) and 5 meter (c) distance from the sensor projected on the Y-Z plane. Colors represent distance to the best-fit plane in centimeters.