Approach for accurate calibration of RGB-D cameras using spheres

Abstract

RGB-D cameras (or color-depth cameras) play key roles in many vision applications. A typical RGB-D camera has only rough intrinsic and extrinsic calibrations that cannot provide the accuracy required in many vision applications. In this paper, we propose a novel and accurate sphere-based calibration framework for estimating the intrinsic and extrinsic parameters of color-depth sensor pair. Additionally, a method of depth error correction is suggested, and the principle of error correction is analyzed in detail. In our method, the feature extraction module can automatically and reliably detect the center and edges of the sphere projection, while excluding noise data and outliers, and the projection of the sphere center on RGB and depth images is used to obtain a closed solution of the initial parameters. Finally, all the parameters are accurately estimated within the framework of nonlinear global minimization. Compared to other state-of-the-art methods, our calibration method is easy to use and provides higher calibration accuracy. Detailed experimental analysis is performed to support our conclusions.

Point Clouds With Color: A Simple Open Library for Matching RGB and Depth Pixels from an Uncalibrated Stereo Pair

“…Liu et al [20] is the more recent work. They use a sphere to calibrate their system RGB-D. Their framework is capable to detect the sphere in the two images.…”

Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments

“…In particular, in the last decade, consumer-grade depth cameras have gained advantage over other sensors, given their low cost, portability, ease of use and measurement accuracy [8]. In brief, an RGB-D camera comprises two parts coupled together to give a dense matrix of pixel values; (a) an RGB camera for providing color information and (b) a depth camera for providing depth information [9]. Consequently, every pixel constructing the image is composed of color and distance values between a view-point and a certain point in the image (RGB-D values).…”

A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras

“…Moreover, the sensor has been the basis of other RGB-D cameras such as Asus Xtion [13], Orbbec Astra 3D [14] or Occipital Structure Sensor [15]. All them are used in many high accuracy applications, including indoor 3D modeling [16][17][18][19], simultaneous localization and mapping (SLAM) [20][21][22], or augmented reality [23], which require a rigorous calibration and error modeling of RGB-D camera data to produce high quality information [24,25].…”

“…Moreover, since deviation and uncertainty of the depth measurement increase with the measurement distance, and the depth measurement error and uncertainty vary across different pixel positions, the depth measurement errors have to be also corrected [26]. To overcome these limitations different strategies to calibrate the intrinsic parameters (focal length, principal point, and lens distortion) of the color and depth cameras and the extrinsic parameters (relative position and orientation) between the cameras have been proposed [8,25,27,28]. For a survey on calibration methods see [29].…”