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A Structured Light RGB-D Camera System for Accurate Depth Measurement 时间:2018-11-02 23:23   来源:未知   作者:admin   点击: 次   Abstract:The ability to reliably measure the depth of the object surface is very important in a range of high-value industries. With the development of 3D vision techniques, RGB-D cameras have been widely used to perform the 6D pose estimation of target objects for a robotic manipulator. Many applications require accurate shape measurements of the objects for 3D template matching. In this work, we develop an RGB-D camera based on the structured light technique with gray-code coding. The intrinsic and extrinsic parameters of the camera system are determined by a calibration process. 3D reconstruction of the object surface is based on the ray triangulation principle. We construct an RGB-D sensing system with an industrial camera and a digital light projector. In the experiments, real-world objects are used to test the feasibility of the proposed technique. The evaluation carried out using planar objects has demonstrated the accuracy of our RGB-D depth measurement system.
1. Introduction
  In recent years, 3D imaging has received a great value in industrial and consumer applications. Machine vision systems developed with 3D imaging allow faster and more accurate measurement of components at manufacturing whereabouts. Nowadays, RGB-D cameras, such as Microsoft Kinect and Asus Xtion, are very popular due to the ability to provide the depth information directly. However, they have the limitation on accuracy and thus are not suitable for the applications that require accurate shape measurements [1–3]. As a result, the development of real-time RGB-D cameras still receives much attention from researchers and practitioners. The objective is to provide highly accurate RGB-D sensing techniques with more effective implementation approaches in terms of the density of acquired point clouds, time consumption, working environment, noise level, etc.
  3D reconstruction based on the structured light technique has been investigated in the past few decades due to its popularity in the manufacturing applications. Structured light systems are suitable solutions for structured light scanning, 3D reconstruction, and 3D sensing with accurate shape measurements [4, 5]. Structured light refers to the process of projecting predesigned known patterns on the scene and capturing the images to calculate the depth for 3D surface reconstruction. It is an important contribution to the development of 3D measurement systems. The patterns projected on the scene can be generated by a projector or other devices [6], and the relationship between the light source and the camera is a crucial factor. The accuracy of 3D reconstruction depends on the correctness of the calibration, which provides the relative pose between the camera and the light source projector.
  In recent literature, several works presented the structured light systems for 3D reconstruction and proposed different approaches to deal with the related problems [7–10]. Scharstein et al. [11] proposed a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Some previous works such as [12–15] described various methods to perform 3D reconstruction and obtained some satisfactory results. However, those techniques require to use precalibrated cameras to find the 3D world coordinates of the projected pattern. Thus, they highly depend on the accuracy of camera calibration and may transfer the error to the projector calibration. In [16], Huang and Tang described a method to perform fast 3D reconstruction using one-shot spatial structured light. Although the method can provide relatively accurate results, the evaluation and analysis were not carried out comprehensively. Some restrictions are also shown in their experiments when performing the tests on complex object surfaces. Cui and Dai [17] proposed a simple and efficient 3D reconstruction algorithm using structured light from 3D computer vision. However, their approach has some limitations on measuring inclined objects, and the 3D information cannot be recovered for the shadow areas.