The technologies of autonomous rendezvous and robotic capturing of non-cooperative targets are very crucial for the future on-orbital service. In this paper, we proposed a method to achieve this aim. Three problems were addressed: the target recognition and pose (position and attitude) measurement based on the stereo vision, the guidance, navigation and control (GNC) of the chaser, and the coordinated plan and control of space robot (CP&C). The pose measurement algorithm includes image filtering, edge detection, line extraction, stereo match and pose computing, et al. Based on the measured values, a certain GNC algorithm was designed for the chaser to approach and rendezvous with the target. Then the CP&C algorithm, which is proved to be advantageous over the traditional separated method, was used to plan and track the trajectories of the base pose and the joint angle. At last, a 3D simulation system was developed to evaluate the proposed method. Simulation results verified the corresponding algorithms.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0263574709990397

Autonomous rendezvous and robotic capturing of non-cooperative target in space

Digital image correlation in experimental mechanics and image registration in computer vision: Similarities, differences and complements

This paper presents a 3D distance measurement accuracy improvement for stereo vision systems using optimization methods A Stereo Vision system is developed and tested to identify common uncertainty sources. As the optimization methods are used to train a neural network, the resulting equation can be implemented in real time stereo vision systems. Computational experiments and a comparative analysis are conducted to identify a training function with a minimal error performance for such method. The offered method provides a general purpose modelling technique, attending diverse problems that affect stereo vision systems. Finally, the proposed method is applied in the developed stereo vision system and a statistical analysis is performed to validate the obtained improvements.

Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods’ approach

A cross-target-based accurate calibration method of binocular stereo systems with large-scale field-of-view

A three-dimensional (3-D) thermogram can provide spatial information; however, it is rarely applied because it lacks an accurate method in obtaining the intrinsic and extrinsic parameters of an infrared (IR) camera. Conventional methods cannot be used for such calibration because an IR camera cannot capture visible calibration patterns. Therefore, in the current study, a trinocular vision system composed of two visible cameras and an IR camera is constructed and a calibration board with miniature bulbs is designed. The two visible cameras compose a binocular vision system that obtains 3-D information from the miniature bulbs while the IR camera captures the calibration board to obtain the two dimensional subpixel coordinates of miniature bulbs. The corresponding algorithm is proposed to calibrate the IR camera based on the gathered information. Experimental results show that the proposed calibration can accurately obtain the intrinsic and extrinsic parameters of the IR camera, and meet the requirements of its application.

Geometric Calibration of IR Camera Using Trinocular Vision

In order to ensure the measurement precision for shape recovery from a stereo vision system, a novel high precision calibration method is presented. Some standard points acquired from the object of interest are used as standard world points. There are four rotation matrices and translation matrices that need to be calibrated, the first is between the left camera and the world coordinate, the second is between the right camera and the world coordinate, the third is between the left camera and the right camera, and the last is between the right camera and the left camera. The lens distortion parameters include radial and tangential distortion. The merits of the calibration method are its high precision, easy of operation, and high reliability.

High precision camera calibration in vision measurement

3D vision measurement is a popular technique to solve non-contact and non-destructive measurement task. In any 3D 
measurement technology, registration is a very important procedure to put two adjacent but different areas into one area. 
And it is a key step to ensure the final 3D measurement precision. Most algorithms used to solve the registration problem 
can be classified to either area-based techniques or feature-based techniques. Feature-based technique is the most 
commonly used method in 3D and 2D registration. Some flags may be pasted or signed on the entities in order to make 
the common area easy to be identified. A lot of researcher focus on feature-based technique, and present some algorithms 
to compute the rotation matrix and translation matrix, to solve the registration problem. But the registration effect is poor 
and the object usually distorts its real shape. We present a novel registration method, without complex algorithms; just 
use the searching merits of computer program. Rotation matrix is computed by three key angles Ψ, θ, and Φ. If these 
angles are found with high precision, the rotation matrix will be confirmed. Then the translation matrix can be calculated 
using the coordinates of three common points. From a lot of experiments, the proposed registration method is proved that 
it is a simple, easy programmed, easy operated, and with high precision. Furthermore, it can be widely used in any other 
3D measurement system.

A simple but high-precision registration method in 3D vision measurement

Computer vision method is an important technology for non-contact and non-destructive measurement. A lot of research
is focusing on the 3D vision technique. Shape from stereo is the most popular method because it can provide high
accuracy and high exactness measurement result. But if the surface is complex, the measurement result is always poor.
So how to make sure the 100% stereo matching rate is an important task for the development of shape from stereo
method. A novel strip matching method, which can ensure 100% matching accuracy, is proposed in this paper. It doesn't
need any other assistant symbol or flag. The proposed dichotomy strip matching method is proved to a technique which
high precision, low costs, easy operation, and an automatically matching method. Furthermore, it can be widely used in
most of 3D vision recovery systems.

Novel dichotomy matching method in 3D machine vision system

Face recognition is an essential work to ensure human safety. It is also an important task in biomedical engineering. 2D
image is not enough for precision face recognition. 3D face data includes more exact information, such as the precision
size of eyes, mouth, etc. 3D face database is an important part in human pattern recognition. There is a lot of method to
get 3D data, such as 3D laser scan system, 3D phase measurement, shape from shading, shape from motion, etc. This
paper will introduce a non-orbit, non-contact, non-laser 3D measurement system. The main idea is from shape from
stereo technique. Two cameras are used in different angle. A sequence of light will project on the face. Human face,
human head, human tooth, human body can all be measured by the system. The visualization data of each person can
form to a large 3D face database, which can be used in human recognition. The 3D data can provide a vivid copy of a
face, so the recognition exactness can be reached to 100%. Although the 3D data is larger than 2D image, it can be used
in the occasion where only few people include, such as the recognition of a family, a small company, etc.

Lu Lu

Papers

High precision camera calibration in vision measurement

A simple but high-precision registration method in 3D vision measurement

Novel dichotomy matching method in 3D machine vision system

3D face database for human pattern recognition