scholarly journals A Real-Time Infrared Stereo Matching Algorithm for RGB-D Cameras’ Indoor 3D Perception

2020 ◽  
Vol 9 (8) ◽  
pp. 472
Author(s):  
Jiageng Zhong ◽  
Ming Li ◽  
Xuan Liao ◽  
Jiangying Qin
Keyword(s):  
Mutual Information ◽  
Stereo Matching ◽  
Low Cost ◽  
Depth Map ◽  
Cost Calculation ◽  
3D Perception ◽  
Detection Range ◽  
Matching Algorithm ◽  
Map Resolution ◽  
The Cost

Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.

scholarly journals Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chen Lv ◽  
Jiahan Li ◽  
Qiqi Kou ◽  
Huandong Zhuang ◽  
Shoufeng Tang
Keyword(s):  
Confidence Interval ◽  
Stereo Matching ◽  
Color Space ◽  
Absolute Difference ◽  
Cost Calculation ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Hsv Color Space ◽  
Census Transform ◽  
The Cost

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.

scholarly journals mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

2021 ◽  
Vol 11 (8) ◽  
pp. 48-57
Author(s):  
A. F. Kadmin ◽  
◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Disparity Map ◽  
Local Method ◽  
Matching Algorithm ◽  
Census Transform ◽  
Traditional Taxonomy ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost ◽  
Vision Algorithm

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

A New Basic Correlation Measurement for Stereo Matching

2019 ◽  
Author(s):  
Bowen Shi ◽  
Shan Shi ◽  
Junhua Wu ◽  
Musheng Chen
Keyword(s):  
Time Complexity ◽  
Stereo Matching ◽  
Computation Method ◽  
Local Correlation ◽  
Matching Algorithm ◽  
Cost Aggregation ◽  
Image Patches ◽  
Disparity Maps ◽  
Aggregation Step ◽  
The Cost

In this paper, we propose a new stereo matching algorithm to measure the correlation between two rectified image patches. The difficulty near objects' boundaries and textureless areas is a widely discussed issue in local correlation-based algorithms and most approaches focus on the cost aggregation step to solve the problem. We analyze the inherent limitations of sum of absolute differences (SAD) and sum of squared differences (SSD), then propose a new difference computation method to restrain the noise near objects' boundaries and enlarge the intensity variations in textureless areas. The proposed algorithm can effectively deal with the problems and generate more accurate disparity maps than SAD and SSD without time complexity increasing. Furthermore, proved by experiments, the algorithm can also be applied in some SAD-based and SSD-based algorithms to achieve better results than the original.

A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder

2014 ◽  
Vol 536-537 ◽  
pp. 67-76
Author(s):  
Xiang Zhang ◽  
Zhang Wei Chen
Keyword(s):  
Stereo Matching ◽  
Cost Calculation ◽  
Stereo Pair ◽  
Disparity Map ◽  
High Definition ◽  
Matching Algorithm ◽  
Census Transform ◽  
Cost Aggregation ◽  
Dsp Builder ◽  
Fpga Chip

This paper proposes a FPGA implementation to apply a stereo matching algorithm based on a kind of sparse census transform in a FPGA chip which can provide a high-definition dense disparity map in real-time. The parallel stereo matching algorithm core involves census transform, cost calculation and cost aggregation modules. The circuits of the algorithm core are modeled by the Matlab/Simulink-based tool box: DSP Builder. The system can process many different sizes of stereo pair images through a configuration interface. The maximum horizon resolution of stereo images is 2048.

scholarly journals Local stereo matching algorithm using modified dynamic cost computation

2021 ◽  
Vol 22 (3) ◽  
pp. 1312
Author(s):  
A. F. Kadmin ◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
T. F. Tg. Wook
Keyword(s):  
Stereo Matching ◽  
Depth Information ◽  
Computation Method ◽  
Local Method ◽  
Matching Algorithm ◽  
Depth Measurement ◽  
Cost Aggregation ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost

Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.

A Stereo Matching System

2013 ◽  
Vol 5 (2) ◽  
pp. 1-8
Author(s):  
Yingpeng Yang
Keyword(s):  
Stereo Matching ◽  
Learning Algorithm ◽  
Moving Average ◽  
Three Dimensional ◽  
Depth Map ◽  
Stereo Image ◽  
Image Feature ◽  
Matching Algorithm ◽  
Depth Cues ◽  
Computer Vision Technology

Determination of the depth of the image feature distinctive automation and other industries of machine vision and computer vision technology in everyday life are becoming increasingly popular. Some techniques have been proposed to extract from the current depth of a 2D image of the feature, which defines a particular object or structure of the information. In many cases, these techniques are automatic, such as a suitable carrier moving average depth identify objects placed in the 2D image. For this intensive depth cues to solve two stereo matching algorithm using a machine learning algorithm. Other methods, relative to the camera based on the motion of the object have been proposed and analyzed by estimating the optical flow calculation depth map. The method of dense and sparse three-dimensional surface of the object to provide the three-dimensional information. This paper discusses the evaluation of the depth cues, through intensive two standard fast algorithm for real-time stereo image matching algorithm.

scholarly journals LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5815
Author(s):  
Nico Podevijn ◽  
Jens Trogh ◽  
Michiel Aernouts ◽  
Rafael Berkvens ◽  
Luc Martens ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Energy Efficient ◽  
Low Cost ◽  
Map Matching ◽  
Directional Information ◽  
Matching Algorithm ◽  
Additional Information ◽  
Energy Impact ◽  
Node Location ◽  
The Cost

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.

scholarly journals Study on Start-Up Process of SAGD by Solvent: Experiment Research and Process Design

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jun Sun ◽  
Yanping Sun ◽  
Chengsheng Wang ◽  
Hui Lin ◽  
Wenchao Zhou ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Physical Simulation ◽  
Low Cost ◽  
Reduction Rate ◽  
Injection Pressure ◽  
Viscosity Reduction ◽  
Cost Calculation ◽  
Core Flooding ◽  
Start Up ◽  
The Cost

Steam-assisted gravity drainage (SAGD) has been used to develop the “super heavy” oil reservoirs in Canada. The viscosity can reach more than 30,000 cp at 50°C. Moreover, owing to their continental deposit origin, the reservoirs have a low porosity and permeability. Because of these challenges, the conventional steam circulation start-up process takes 6 to 12 months before the well pair can be switched to production. Solvent has been used to start-up SAGD with success. But now, low price of oil and high cost of solvent make solvent-assisted start-up process limited. This paper applies experimental schemes, such as viscosity reduction rate evaluation, core flooding, and 3D physical simulation, tests solvent performance, optimizes process parameters, and designs process solutions. Apply numerical simulation to test solvent-assisted SAGD start-up effect and calculate the cost. This paper researches a unique low-cost solvent compare with xylene. The basic properties and core flood experiment show that the two solvents are similar with viscosity reduction rate, asphalt dissolution rate, and injection pressure, and the price of solvent is 18% lower. The 3D model experiment shows that the average start-up time is reduced by 15%, and steam injection volume is reduced by 21.4%. The numerical simulation results show that without solvent, it will take 180 d for start-up process, and with solvent, the time has reduced by 50% and takes 90 days. Cost calculation results show that the cost will reduce 18% by solvent compared to xylene. Moreover, the production rate has been improved in production stage. This paper applies a 3D physical model to simulate the solvent-assisted SAGD start-up process. Research conclusions show the start-up mechanism of solvent and the process of temperature change of steam chamber.

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