Spatial uncertainty model for visual features using a Kinect™ sensor

• Published in: Sensors (Basel, Switzerland) Vol. 12; no. 7; pp. 8640 - 8662
• Main Authors: Park, Jae-Han, Shin, Yong-Deuk, Bae, Ji-Hun, Baeg, Moon-Hong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2012; Molecular Diversity Preservation International (MDPI)
• Subjects: 3D acquisition; Calibration; Cameras; Covariance matrix; depth calibration; depth sensing camera; disparity map; Kinect™ sensor; Mathematical analysis; Mathematical models; point cloud; Propagation; Quantitative analysis; Robotics; Sensors; Three dimensional; Uncertainty; uncertainty model; Visual; visual feature; visual feature; depth calibration; disparity map; 3D acquisition; Kinect™ sensor; depth sensing camera; point cloud; uncertainty model
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s120708640

This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.