A Linear Approach for Depth and Colour Camera Calibration Using Hybrid Parameters

Abstract

Many recent applications of computer graphics and human computer interaction have adopted both colour cameras and depth cameras as input devices. Therefore, an effective calibration of both types of hardware taking different colour and depth inputs is required. Our approach removes the numerical difficulties of using non-linear optimization in previous methods which explicitly resolve camera intrinsics as well as the transformation between depth and colour cameras. A matrix of hybrid parameters is introduced to linearize our optimization. The hybrid parameters offer a transformation from a depth parametric space (depth camera image) to a colour parametric space (colour camera image) by combining the intrinsic parameters of depth camera and a rotation transformation from depth camera to colour camera. Both the rotation transformation and intrinsic parameters can be explicitly calculated from our hybrid parameters with the help of a standard QR factorisation. We test our algorithm with both synthesized data and real-world data where ground-truth depth information is captured by Microsoft Kinect. The experiments show that our approach can provide comparable accuracy of calibration with the state-of-the-art algorithms while taking much less computation time (1/50 of Herrera's method and 1/10 of Raposo's method) due to the advantage of using hybrid parameters.

Spatial Localization of EEG Electrodes in a TOF+CCD Camera System

“…Traditionally, the calibration method utilizes the depth map obtained by the TOF camera and the color map obtained by the CCD camera to complete calibration (Cheng et al, 2016; Raposo et al, 2016). Nevertheless, the resolution of the depth image is very low, and the results are often unstable.…”

“…With regard to the point cloud stitching problem, many works use an ICP algorithm or improved ICP algorithm (Cheng et al, 2016; Yang et al, 2016). However, here, due to the large deviation of the angle of view, the performance of ICP algorithm is not ideal, thus SVD is adopted to calculate the conversion relationship between the two sets of point clouds (Sorkine, 2009; Jung et al, 2015; Raposo et al, 2016).…”

Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras

“…In Formula (4), and are the internal parameters of the depth camera, can be obtained by the depth information, and then we can get the original disparity estimated value . The method of [ 30 ] can be used to obtain the parameter in Equation (5), and the original disparity measurement value can also be obtained. At this point, we have a preliminary cost function: where , and are the variances of the measurement error of color camera, depth camera and external camera, respectively.…”

Joint kinect and multiple external cameras simultaneous calibration

“…In the formula (4), we can get the original disparity estimated value dˆ. the method of [26] can be used to obtain the parameter kk Z in equation (5), then, the original disparity measurement value d can also be obtained. At this point, we have a preliminary cost function: σ are the variances of the measurement error of color camera, depth camera and external camera respectively.…”