A real-time image matching algorithm for integrated navigation system

Optik ◽  
2014 ◽  
Vol 125 (16) ◽  
pp. 4434-4436 ◽  
Author(s):  
Chao Huang ◽  
Weidong Zhou
Keyword(s):  
Real Time ◽  
Navigation System ◽  
Image Matching ◽  
Integrated Navigation ◽  
Matching Algorithm ◽  
Integrated Navigation System ◽  
Real Time Image ◽  
Time Image

A Real-Time Image Matching Algorithm for Binocular Stereo Measurement System

2019 ◽  
Author(s):  
Jianyu Duan ◽  
Lingyu Sun ◽  
Lijun Li ◽  
Zongmiao Dai ◽  
Zhenkai Xiong ◽  
...  
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Image Matching ◽  
Great Influence ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Real Time Image ◽  
Binocular Stereo ◽  
Time Image

Abstract Binocular stereo measurement system can obtain accurate three-dimensional information from two-dimensional images. It has been widely applied in many fields such as vehicle tracking, robot navigating, automatic crane lifting, as well as other fields. The crucial step of binocular stereo measurement is image matching. For the image matching, it is a great challenge to ensure both real-time and matching accuracy simultaneously. The image matching algorithm has a great influence on the image matching time and accuracy. In this paper, a real-time image matching algorithm for binocular stereo measurement system is proposed based on Speedup Robust Features (SURF) algorithm. In the proposed algorithm, firstly, the key feature points are identified by the original SURF algorithm method. Secondly, the main direction of the key feature point is determined by intensity centroid method. Then, the feature descriptor is calculated by the BRIEF binary method so that the time of feature description can be shortened. Finally, RANSAC (Random Sample Consensus) method is adopted to remove mismatching points. The experiments results show that the proposed algorithm can shorten image matching time obviously and improve the accuracy of matching points.

scholarly journals Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 198 ◽  
Author(s):  
Zilong Deng ◽  
Dongxiao Yang ◽  
Xiaohu Zhang ◽  
Yuguang Dong ◽  
Chengbo Liu ◽  
...  
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Image Matching ◽  
Image Guidance ◽  
Feature Matching ◽  
Guidance System ◽  
Stabilization Method ◽  
Image Stabilization ◽  
Real Time Image ◽  
Time Image

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.

High-Speed and Robust Scene Matching Algorithm Based on ORB for SAR/INS Integrated Navigation System

2012 ◽  
Vol 241-244 ◽  
pp. 439-443
Author(s):  
Fang Chen ◽  
Yun Xi Xu
Keyword(s):  
Navigation System ◽  
High Speed ◽  
Hamming Distance ◽  
Reference Image ◽  
Integrated Navigation ◽  
Sar Images ◽  
Matching Algorithm ◽  
Integrated Navigation System ◽  
Scene Matching ◽  
Position Errors

It is important that scene matching algorithm should satisfy the requirements of real-time, robustness and high-precision for inertial integrated navigation system. And considering the serious distortion and speckle noises of SAR images, we proposed a new scene matching algorithm for the SAR/INS integrated navigation system with high-speed and robustness based on Oriented FAST and Rotated BRIEF (ORB). We started by detecting scale-space FAST-based features in combination with an efficiently computed orientation in the image. Then, we calculated feature point's Rotation-Aware BRIEF descriptor which performs well with rotation and match features by computing Hamming distance between descriptors. Finally, we adopted GroupSAC which are proposed recently to remove the false matching points and the least square algorithm for getting the distortion transformation parameters that are the aircraft position errors and rotation transform parameters between real image and reference image. Experimental results on real SAR images indicate that our algorithm is invariant to various image transformations due to rotation and scale, and also robust to speckle noise and extremely efficient to compute, better than SIFT in many situations. Therefore, our algorithm can meet the high performance needs for matching navigation in the SAR/INS integrated navigation system.

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