Dynamic errors, representing the variance of the estimation results, can be improved by filtering methods. However, results for a mathematical selleckchem uncertainty model representing Inhibitors,Modulators,Libraries the covariance matrix form for the spatial measurements of visual features using Kinect? sensors Inhibitors,Modulators,Libraries are unavailable. Khoshelham and Elberink [16] presented an error model and its analysis results; however, these results were represented as an independent error model with respect Inhibitors,Modulators,Libraries to the X, Y and Z axis, and not as a covariance matrix. In the Cartesian space, the errors in the X, Y and Z axis data are correlated with each other; thus, the covariance matrix is not in a diagonal form. Therefore, we would like to derive the spatial uncertainty model of visual features using Kinect? sensors, which is represented by the covariance matrix for 3D measurement errors in the actual Cartesian space.
To achieve this objective, we derive the propagation relationship of the uncertainties between the disparity image space Inhibitors,Modulators,Libraries and the real Cartesian space with the mapping function between the two spaces. Then, we obtain the mathematical model for the covariance matrix of the spatial measurement error by using the propagation relationship. Finally, a quantitative analysis of the spatial measurement of Kinect? sensors is performed by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model.2.?3D Reconstruction from Kinect? Sensor DataKinect? sensors provide disparity image and RGB image information. The disparity image represents the spatial information, and the RGB image represents the color information.
3D point cloud data, which contains color information, can be obtained by fusing the disparity image and the RGB image information. Figure 1 shows the disparity image, the RGB image, and the colored 3D point cloud information that was reconstructed from a Kinect? sensor. Disparity image data, containing information about the distance of the location of each pixel, is expressed Drug_discovery as an integer from 0 to 2,047. This data contains relative distance information, which does not represent metric information. In addition, the relationship between distance and disparity image data is non-linear, as shown in the graph in Figure 2. Thus, the depth calibration function, which can transform disparity image data into actual distance information, is needed in order to reconstruct 3D information using Kinect sensors.
Figure 1.Information from Kinect? sensor. (a) Disparity map image; (b) RGB image; (c) Colored 3D point cloud data.Figure 2.Relationship between the disparity image and the real depth information (disparity: 400�C1,069, real depth: 0.5�C17.3 m).The mathematical model between disparity image data d and real Sunitinib Sutent depth is represented by Equation (1) [16]. In this equation, Zo, fo, and b indicate the distance of the reference pattern, focal length, and base length respectively.