Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

Abstract

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.

Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming

“…As it can be seen in Figure 11(C), branches and trunks can be detected in the lighting conditions of the experiment. It is important to note that, despite the fact that leaves, flowers, and branches/trunks were classified using different templates, the results can be combined and by means of clustering algorithms (such as the ones shown in Park et al (2012)) it is possible to obtain important information from the grove. For example, in the case shown in Figure 11, our approach has Figure 10 Leaf detection from an apple tree grove using the RGB-D information.…”

“…Therefore, once known the locations of the Kinect sensors, the Cartesian characterization of the fruits (with respect to a previously defined reference frame) can be easily obtained (Park et al, 2012). Two sensors are needed for the robustness of the detection process.…”

RGB–D terrain perception and dense mapping for legged robots

“…The uncertainty model is based on the solution proposed by Park et al (2012). First, the relation between the position of a pixel in the depth image and the 3D position of the point is defined:…”

Kinect unbiased

“…From the hardware, communication and capabilities side the work from Freenect has been crucial [5]. Several papers have analyzed Kinect and 978-1-4799-5751-4/14/$31.00 ©2014 IEEE have compared it against other depth range alternatives [6][7][8][9], those helped to identify the bias that Kinect systems show against temperature changes. Finally some groups have analyzed the pattern and the optical characteristics [10].…”