scholarly journals UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 67-70
Author(s):  
X. G. Li ◽  
C. Ren ◽  
T. X. Zhang ◽  
Z. L. Zhu ◽  
Z. G. Zhang
Keyword(s):  
Image Matching ◽  
Consensus Algorithm ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Invariant Feature ◽  
Aerial Vehicle ◽  
Random Sample Consensus ◽  
Uav Image ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.

Merging grid maps of different resolutions by scaling registration

Robotica ◽  
2015 ◽  
Vol 34 (11) ◽  
pp. 2516-2531 ◽  
Author(s):  
Liang Ma ◽  
Jihua Zhu ◽  
Li Zhu ◽  
Shaoyi Du ◽  
Jingru Cui
Keyword(s):  
Objective Function ◽  
Superior Performance ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Consistent Feature ◽  
Initial Transformation ◽  
Random Sample Consensus ◽  
Sift Features ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

SUMMARYThis paper considers the problem of merging grid maps that have different resolutions. Because the goal of map merging is to find the optimal transformation between two partially overlapping grid maps, it can be viewed as a special image registration issue. To address this special issue, the solution considers the non-common areas and designs an objective function based on the trimmed mean-square error (MSE). The trimmed and scaling iterative closest point (TsICP) algorithm is then proposed to solve this well-designed objective function. As the TsICP algorithm can be proven to be locally convergent in theory, a good initial transformation should be provided. Accordingly, scale-invariant feature transform (SIFT) features are extracted for the maps to be potentially merged, and the random sample consensus (RANSAC) algorithm is employed to find the geometrically consistent feature matches that are used to estimate the initial transformation for the TsICP algorithm. In addition, this paper presents the rules for the fusion of the grid maps based on the estimated transformation. Experimental results carried out with publicly available datasets illustrate the superior performance of this approach at merging grid maps with respect to robustness and accuracy.

scholarly journals Pose detection of parallel robot based on improved RANSAC algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 855-868 ◽  
Author(s):  
Guo-Qin Gao ◽  
Qian Zhang ◽  
Shu Zhang
Keyword(s):  
Real Time ◽  
Parallel Robot ◽  
Feature Points ◽  
Scale Invariant ◽  
Time Performance ◽  
Invariant Feature ◽  
Pose Detection ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.

A COMPARATIVE STUDY OF SOFTWARE FILTERS APPLIED AS A PREVIOUS STEP OF THE ICP ALGORITHM IN ROBOT LOCATION

2014 ◽  
Vol 23 (08) ◽  
pp. 1450118 ◽  
Author(s):  
ALEJANDRO HIDALGO-PANIAGUA ◽  
MIGUEL A. VEGA-RODRÍGUEZ ◽  
NIEVES PAVÓN ◽  
JOAQUÍN FERRUZ
Keyword(s):  
Comparative Study ◽  
Execution Time ◽  
Computational Time ◽  
Scale Invariant ◽  
Icp Algorithm ◽  
Invariant Feature ◽  
Random Sample Consensus ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Previous Step

In the field of robotics, one of the essential tasks for a robot to accomplish its goals is to know its own location. The localization problem is a complex task and, usually, different systems, methods and sensors are needed to achieve it. One of these methods is the iterative closest points (ICP) algorithm. The main problem that ICP presents is its high computational time. Due to this, it is common to filter its input data before calculating the final transformation. In this paper, a comparative study in terms of precision and execution time among the most popular filters used in combination with the ICP is presented. The study indicates that the scale-invariant feature transform (SIFT) is the filter that better improves the execution time, while its combination with the RANdom SAmple Consensus (RANSAC) obtains better precision results.

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