scholarly journals Painting Path Planning for a Painting Robot with a RealSense Depth Sensor

2021 ◽  
Vol 11 (4) ◽  
pp. 1467
Author(s):  
Vladimir Tadic ◽  
Akos Odry ◽  
Ervin Burkus ◽  
Istvan Kecskes ◽  
Zoltan Kiraly ◽  
...  
Keyword(s):  
Path Planning ◽  
Random Sample ◽  
Consensus Model ◽  
Depth Sensor ◽  
Mapping Procedure ◽  
Stereo Cameras ◽  
Painting Robot ◽  
Random Sample Consensus ◽  
Robotic Applications ◽  
Ransac Algorithm

The utilization of stereo cameras in robotic applications is presented in this paper. The use of a stereo depth sensor is a principal step in robotics applications, since it is the first step in sequences of robotic actions where the intent is to detect and extract windows and obstacles that are not meant to be painted from the surrounding wall. A RealSense D435 stereo camera was used for surface recording via a real-time, appearance-based (RTAB) mapping procedure, as well as to navigate the painting robot. Later, wall detection and the obstacle avoidance processes were performed using statistical filtering and a random sample consensus model (RANSAC) algorithm.

scholarly journals P-RANSAC: An Integrity Monitoring Approach for GNSS Signal Degraded Scenario

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Gaetano Castaldo ◽  
Antonio Angrisano ◽  
Salvatore Gaglione ◽  
Salvatore Troisi
Keyword(s):  
Computer Vision ◽  
Random Sample ◽  
Simultaneous Presence ◽  
Static Test ◽  
Navigation Solution ◽  
Processing Data ◽  
Urban Scenario ◽  
Random Sample Consensus ◽  
Quality Check ◽  
Ransac Algorithm

Satellite navigation is critical in signal-degraded environments where signals are corrupted and GNSS systems do not guarantee an accurate and continuous positioning. In particular measurements in urban scenario are strongly affected by gross errors, degrading navigation solution; hence a quality check on the measurements, defined as RAIM, is important. Classical RAIM techniques work properly in case of single outlier but have to be modified to take into account the simultaneous presence of multiple outliers. This work is focused on the implementation of random sample consensus (RANSAC) algorithm, developed for computer vision tasks, in the GNSS context. This method is capable of detecting multiple satellite failures; it calculates position solutions based on subsets of four satellites and compares them with the pseudoranges of all the satellites not contributing to the solution. In this work, a modification to the original RANSAC method is proposed and an analysis of its performance is conducted, processing data collected in a static test.

RANdom sample consensus (RANSAC) algorithm for enhancing overlapped etched track counting

2015 ◽  
Vol 9 (2) ◽  
pp. 97-106 ◽  
Author(s):  
Mitra Ghergherehchi ◽  
Yoon Sang Kim ◽  
Seung Yeol Kim ◽  
Hossein Afarideh
Keyword(s):  
Random Sample ◽  
Random Sample Consensus ◽  
Track Counting ◽  
Ransac Algorithm

A Method for UAV’s Images Seamless Stitching

2014 ◽  
Vol 556-562 ◽  
pp. 5076-5080
Author(s):  
Peng Jun Li ◽  
Jian Zeng Li
Keyword(s):  
Random Sample ◽  
Nearest Neighbors ◽  
Experimental Results ◽  
Image Stitching ◽  
Feature Points ◽  
K Nearest Neighbors ◽  
Fusion Technique ◽  
Speeded Up Robust Features ◽  
Random Sample Consensus ◽  
Ransac Algorithm

Image stitching is an important technology to build a panorama image by combing several images with overlapped areas. In this study, we develop a image seamless mosaic and fusion technique to obtain a prefect panorama image after stitching. At first, it is usingspeeded-up robust features(SURF) algorithm to extract features form the images for stitching. Then,k-nearest neighbors(KNN) method is used to match the feature points andRandom sample consensus(RANSAC) algorithm is used to clear them. Thirdly, a method is improved to achieve seamless stitching based on optimal suture of the overlapped areas. Experimental results indicate that this method can eliminate cohesion gap of two stitching images very well.

Optimize Fundamental Matrix Estimation Based on RANSAC

2011 ◽  
Vol 50-51 ◽  
pp. 333-337
Author(s):  
Jun Zhou
Keyword(s):  
Computer Vision ◽  
Random Sample ◽  
Robust Estimation ◽  
Structure From Motion ◽  
Fundamental Matrix ◽  
Estimation Algorithm ◽  
Matrix Estimation ◽  
Random Sample Consensus ◽  
M Estimation ◽  
Ransac Algorithm

Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion, and which is especially difficult since it is often based on correspondences that are spoilt by noise and outliers. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this article, we provide an approach for improve of RANSAC that has the benefit of offering fast and accurate RANSAC, and combine the M-estimation algorithm get the fundamental matrix. Experimental results are given that support the adopted approach and demonstrate the algorithm is a practical technique for fundamental matrix estimation.

Epipolar Geometry Estimation Using Improved LO-RANSAC

2011 ◽  
Vol 213 ◽  
pp. 255-259
Author(s):  
Jun Zhou
Keyword(s):  
Computer Vision ◽  
Random Sample ◽  
Robust Estimation ◽  
Epipolar Geometry ◽  
Large Subset ◽  
Geometry Estimation ◽  
Random Sample Consensus ◽  
And Robotics ◽  
Ransac Algorithm

The estimation of the epipolar geometry is of great interest for a number of computer vision and robotics tasks, and which is especially difficult when the putative correspondences include a low percentage of inliers correspondences or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for improve of locally optimized RANSAC (LO-RANSAC) that has the benefit of offering fast and accurate RANSAC. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the LO-RANSAC algorithms.

scholarly journals RIGID TRACKING FOR SCALE AND ROTATION VARYING TARGETS FROM MOVING CAMERA

2021 ◽  
Vol 9 (2) ◽  
pp. 175-180
Author(s):  
Karthika Pragadeeswari C., Yamuna G.
Keyword(s):  
Feature Extraction ◽  
Optical Flow ◽  
Random Sample ◽  
Computation Time ◽  
Rotation Invariance ◽  
Histogram Of Oriented Gradients ◽  
True Positive ◽  
Random Sample Consensus ◽  
Time Accuracy ◽  
Ransac Algorithm

Targets when move rapidly needed to be tracked in many significant fields such as in combat applications. Objects undergoes many scale changes and also undergoes rotation variance. The target when viewed from static position, the size becomes smaller as the target moves farther and farther. Tracking the targets needs more attention and this can be done by Improved optical flow to which feature extraction through Histogram of Oriented Gradients and Random Sample Consensus (RANSAC) algorithm for scale and rotation invariance is added. The performance of the method is measured by its computation time, accuracy and high true positive values and other related parameters simulated in MAT LAB.

scholarly journals Random Sample Consensus (RANSAC) Algorithm, A Generic Implementation

2010 ◽  
Author(s):  
Ziv Yaniv
Keyword(s):  
Random Sample ◽  
Fundamental Matrix ◽  
Value Estimation ◽  
Robust Parameter ◽  
Random Sample Consensus ◽  
Data Elements ◽  
Parameter Values ◽  
Ransac Algorithm

The Random Sample Consensus (RANSAC) algorithm for robust parameter value estimation has been applied to a wide variety of parametric entities (e.g. plane, the fundamental matrix). In many implementations the algorithm is tightly integrated with code pertaining to a specific parametric object. In this paper we introduce a generic RANSAC implementation that is independent of the estimated object. Thus, the user is able to ignore outlying data elements potentially found in their input. To illustrate the use of the algorithm we implement the required components for estimating the parameter values of a hyperplane and hypersphere.

scholarly journals Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1166
Author(s):  
Wei Zhang ◽  
Liang Gong ◽  
Suyue Chen ◽  
Wenjie Wang ◽  
Zhonghua Miao ◽  
...  
Keyword(s):  
Field Experiments ◽  
Absolute Error ◽  
Identification Accuracy ◽  
Depth Image ◽  
Dynamic Identification ◽  
Random Sample Consensus ◽  
Harvesting Efficiency ◽  
Value Decomposition ◽  
Forage Harvester ◽  
Ransac Algorithm

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

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