A novel 1D target-based calibration method with unknown orientation for structured light vision sensor

The problem associated with calibrating a structured light vision sensor is that it is difficult to obtain the coordinates of the world calibration points falling on the light stripe plane. In this paper, we present a novel method to address this problem by randomly moving a 1D (one-dimension) target within the sensor's view field. At each position where the target is set, the world coordinates with one calibration point on the light stripe plane that can be obtained based on at least three preset known points on the 1D target by a proposed two-stage technique. Thus, as long as the 1D target is at least set at three different positions, not less than three such calibration points can be obtained to perform the structured light vision sensor calibration. The simulation and real experiments conducted reveal that the proposed approach has an accuracy of up to 0.065 mm. The advantages of the proposed method are: (1) 1D target is easily machined with high accuracy, which reduces the cost of the calibration equipment; (2) the method simplifies the calibration operation and can be convenient in on-site calibration; (3) the method is suitable for use in confined spaces.     

Line structured light vision sensor calibration using parallel straight lines features

Line structured light vision sensor (LSLVS) calibration is to establish the relation between the camera and the light plane projector. This paper proposes a geometrical calibration method for LSLVS via three parallel straight lines on a 2D target. The approach is based on the properties of vanishing points and lines. During the calibration, one important aspect is to determine the normal vector of the light plane, another critical step is to obtain the distance parameter d of the light plane. In this paper, we put the emphasis on the later one. The distance constraint of parallel straight lines is used to compute a 3D feature point on the light plane, resulting in the acquisition of the parameter d. Thus, the equation of the light plane in the camera coordinate frame (CCF) can be solved out. To evaluate the performance of the algorithm, possible factors affecting the calibration accuracy are taken into account. Furthermore, mathematical formulations for error propagation are derived. Both computer simulations and real experiments have been carried out to validate our method, and the RMS error of the real calibration reaches 0.134 mm within the field of view 500 mm × 500 mm.     

A novel calibration approach to structured light 3D vision inspection

Structured light 3D vision inspection is a commonly used method for various 3D surface profiling techniques. In this paper, a novel approach is proposed to generate the sufficient calibration points with high accuracy for structured light 3D vision. This approach is based on a flexible calibration target, composed of a photo-electrical aiming device and a 3D translation platform. An improved algorithm of back propagation (BP) neural network is also presented, and is successfully applied to the calibration of structured light 3D vision inspection. Finally, using the calibration points and the improved algorithm of BP neural network, the best network structure is established. The training accuracy for the best BP network structure is 0.083 mm, and its testing accuracy is 0.128 mm.     

基于圆结构光视觉传感器的标定特征点获取方法

管道作为工业生产重要的传输手段其内表面腐蚀程度和瑕疵的精确检测对于保证安全生产具有重要意义。针对管道内表面圆结构光视觉检测,提出了一种基于共面参照物获取圆结构光视觉传感器标定特征点的新方法。该方法设计了圆结构光平面靶标,基于交比不变原理,以摄像机三维坐标系为中介,将多个局部世界坐标系下的标定特征点统一到全局世界坐标系中,得到位于圆结构光曲面上的非共线标定特征点的三维世界坐标。该方法降低了标定设备的成本,简化了结构光视觉传感器的标定过程。标定实验精度达到0.340 mm,标定结果表明,该方法切实可行。