Calibration Of Stereo Vision System Research Articles

Integration of stereo vision system calibration and kinematic calibration for an autonomous kiwifruit harvesting system

Stereo vision system and manipulator are two major components of an autonomous fruit harvesting system. In order to raise the fruit-harvesting rate, stereo vision system calibration and kinematic calibration are two significant processes to improve the positional accuracy of the system. This article reviews the mathematics of these two calibration processes and presents an integrated approach for acquiring calibration data and calibrating both components of an autonomous kiwifruit harvesting system. The calibrated harvesting system yields good positional accuracy in the laboratory tests, especially in harvesting individual kiwifruit. However, the performance is not in line with the outcomes in the orchard field tests due to the cluster growing style of kiwifruit. In the orchard test, the calibrations reduce the fruit drop rate but it does not impressively raise the fruit harvesting rate. Most of the fruit in the clusters remain in the canopy due to the invisibility of the stereo vision system. After analyzing the existing stereo vision system, a future visual sensing system research direction for an autonomous fruit harvesting system is justified.

Accurate stereo vision system calibration with chromatic concentric fringe patterns.

Camera calibration is used to determine the intrinsic and extrinsic parameters of a 3D imaging system based on structured light. Traditional methods like chessboard and circular dots usually employ an intensity-based feature point detection procedure, and are susceptible to noise, image contrast, and image blur. To address these issues, we proposed an active calibration method to accurately detect the centers of chromatic concentric fringe patterns (CCFP). Specifically, we first acquired the circular phase using a phase analysis algorithm, then extracted nine phase contours from the circular phase for the corresponding subpixel center coordinates using an ellipse fitting algorithm, and precisely calculated the final center with their weighted sum. We ran a simulation and evaluated the impacts of different degrees of Gaussian blur and noise on the calibrated parameters. The simulation demonstrates that our approach is more robust to noise and blur than previous ones, and our approach yields a higher calibration accuracy. Moreover, we carried out a comparison experiment to evaluate the performance of our method. It showed that the reprojection error can be reduced by at least 10% in the out-of-focus condition (i.e., the target is beyond the working distance of the camera) and the 3D reconstruction accuracy can be improved by nearly 10%.

Experiments on mobile robot stereo vision system calibration under hardware imperfection

Calibration is essential for any robot vision system for achieving high accuracy in deriving objects metric information. One of typical requirements for a stereo vison system in order to obtain better calibration results is to guarantee that both cameras keep the same vertical level. However, cameras may be displaced due to severe conditions of a robot operating or some other circumstances. This paper presents our experimental approach to the problem of a mobile robot stereo vision system calibration under a hardware imperfection. In our experiments, we used crawler-type mobile robot «Servosila Engineer». Stereo system cameras of the robot were displaced relative to each other, causing loss of surrounding environment information. We implemented and verified checkerboard and circle grid based calibration methods. The two methods comparison demonstrated that a circle grid based calibration should be preferred over a classical checkerboard calibration approach.

The Application of Vision Principle in 3D Reconstruction

3D reconstruction technology provides digital model of the actual product for the CAD/CAM system of the reverse engineering, which has practical significance for promoting the development of manufacturing automation. Meanwhile, the digital models through 3D reconstruction have the important applications in the fields of virtual reality, video conferencing, criminal investigation and cosmetic surgery. Especially the research for 3D reconstruction of freedom surface is an important research topic in the computer graphics. In this paper, the technology of 3D reconstruction based on stereo vision principle is presented, which can effectively realize reconstruction of 3D digital model of freedom surface. Firstly, the experimental system of stereo vision is established consisting of two CCD cameras in accordance with the principle of binocular stereo vision. Then, the calibration of stereo vision system is completed by selecting the BP neural network for simulating the mapping relation between the images in left and right imaging planes and 3D spatial objects after comparing the two calibration methods of genetic algorithm and BP neural network. Finally, the vision system is experimentally verified taking the face of plaster and human foot as examples, and very good results are achieved.

Research of Stereo Matching Based on HALCON

Binocular stereo matching is a hot and difficult problem in machine vision. In this paper, based on the matching method of Halcon which is visual software perform image matching. First, performing binocular stereo vision system calibration, based on the calibration results acquired the epipolar standard geometric structure. Then, image matching researched under this structure. At last, using ncc matching algorithm, through comparing the different parameters matching window obtain ideal match results. Experiments prove that this method not only can effectively shorten matching time, but also can achieve higher matching accuracy.

A Fast Linear Calibration of Computer Stereo Vision System

Computer stereo vision has been paid high attention in areas such as industry measurement and automatic control. Calibration of stereo vision system is a key step to implement vision measurement. Nonlinear calibration method involves too many parameters and too complex function, so the processing of optimization is difficult and slow. In this paper, two digital cameras are used to set up stereo vision system which provides convenient experimental operation. The binocular stereo vision system is calibrated using linear calibration model. The results indicate that the method is stable and fast with high precision and practicability.

Improving the performance of double-plane stereo vision system calibration, using a virtual plane

Abstract In the double-plane method for stereo vision system calibration, the correspondence between screen coordinates and location in 3D space is calculated based on four plane-to-plane transformations; there are two planes of the calibration pattern and two cameras. The method is intuitive, and easy to implement, but, the main disadvantage is ill-conditioning for some spatial locations. In this paper we propose a method which exploits the third plane which physically does not belong to the calibration pattern, but can be calculated from the set of reference points. Our algorithm uses a combination of three calibration planes, with weights which depend on screen coordinates of the point of interest; a pair of planes which could cause numerical errors receives small weights and have practically no influence on the final results. We analyse errors, and their distribution in 3D space, for the basic and the improved algorithm. Experiments demonstrate high accuracy and reliability of our method compared to the basic version; root mean square error and maximum error, are reduced by factors of 4 and 20 respectively.

A Technique for Binocular Stereo Vision System Calibration by the Nonlinear Optimization and Calibration Points with Accurate Coordinates

With the increasing need for higher accuracy measurement in computer vision, the precision of camera calibration is a more important factor. The objective of stereo camera calibration is to estimate the intrinsic and extrinsic parameters of each camera. We presented a high-accurate technique to calibrate binocular stereo vision system having been mounted the locations and attitudes, which was realized by combining nonlinear optimization method with accurate calibration points. The calibration points with accurate coordinates, were formed by an infrared LED moved with three-dimensional coordinate measurement machine, which can ensure indeterminacy of measurement is 1/30000. By using bilinear interpolation square-gray weighted centroid location algorithm, the imaging centers of the calibration points can be accurately determined. The accuracy of the calibration is measured in terms of the accuracy in the reconstructing calibration points through triangulation, the mean distance between reconstructing point and given calibration point is 0.039mm. The technique can satisfy the goals of measurement and camera accurate calibration.