scholarly journals Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3747 ◽  
Author(s):  
Ma ◽  
Bai ◽  
Wang ◽  
Fang
Keyword(s):  
Nonlinear Optimization ◽  
Stereo Matching ◽  
Measurement Model ◽  
Stereo Image ◽  
Measurement Unit ◽  
Experimental Comparison ◽  
The Public ◽  
Stereo Cameras ◽  
Image Pairs ◽  
The Cost

The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.

scholarly journals Efficient Stereo Matching with Decoupled Dissimilarity Measure Using Successive Weighted Summation

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Cheng-Tao Zhu ◽  
Yau-Zen Chang ◽  
Huai-Ming Wang ◽  
Kai He ◽  
Shih-Tseng Lee ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Dissimilarity Measure ◽  
Stereo Image ◽  
Fitting Procedure ◽  
Comparable Performance ◽  
Novel Method ◽  
Disparity Maps ◽  
Image Pairs ◽  
Benchmark Evaluation ◽  
Complexity Algorithm

Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps.

scholarly journals Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

2019 ◽  
Vol 9 (15) ◽  
pp. 3122 ◽  
Author(s):  
Chengtao Zhu ◽  
Yau-Zen Chang
Keyword(s):  
Stereo Matching ◽  
Image Filtering ◽  
Data Sets ◽  
Matching Accuracy ◽  
Edge Preserving ◽  
Novel Approach ◽  
Combination Scheme ◽  
Image Pairs ◽  
Guided Image Filtering ◽  
The Cost

Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.

scholarly journals Real-Time FPGA Accelerated Stereo Matching for Temporal Statistical Pattern Projector Systems

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6435
Author(s):  
Zan Brus ◽  
Marko Kos ◽  
Matic Erker ◽  
Iztok Kramberger
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Input Image ◽  
Stereo Image ◽  
Image Pair ◽  
Performance Per Watt ◽  
Statistical Pattern ◽  
And Performance ◽  
Image Pairs ◽  
High Level

The presented paper describes a hardware-accelerated field programmable gate array (FPGA)–based solution capable of real-time stereo matching for temporal statistical pattern projector systems. Modern 3D measurement systems have seen an increased use of temporal statistical pattern projectors as their active illumination source. The use of temporal statistical patterns in stereo vision systems includes the advantage of not requiring information about pattern characteristics, enabling a simplified projector design. Stereo-matching algorithms used in such systems rely on the locally unique temporal changes in brightness to establish a pixel correspondence between the stereo image pair. Finding the temporal correspondence between individual pixels in temporal image pairs is computationally expensive, requiring GPU-based solutions to achieve real-time calculation. By leveraging a high-level synthesis approach, matching cost simplification, and FPGA-specific design optimizations, an energy-efficient, high throughput stereo-matching solution was developed. The design is capable of calculating disparity images on a 1024 × 1024(@291 FPS) input image pair stream at 8.1 W on an embedded FPGA platform (ZC706). Several different design configurations were tested, evaluating device utilization, throughput, power consumption, and performance-per-watt. The average performance-per-watt of the FPGA solution was two times higher than in a GPU-based solution.

scholarly journals REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

2016 ◽  
Vol III-3 ◽  
pp. 123-130 ◽  
Author(s):  
M. Shahbazi ◽  
G. Sohn ◽  
J. Théau ◽  
P. Ménard
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Search Space ◽  
Image Resolution ◽  
Stereo Image ◽  
Digital Cameras ◽  
Dense Stereo Matching ◽  
Dense Matching ◽  
Optimization Stage ◽  
Image Pairs

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

scholarly journals REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

2016 ◽  
Vol III-3 ◽  
pp. 123-130
Author(s):  
M. Shahbazi ◽  
G. Sohn ◽  
J. Théau ◽  
P. Ménard
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Search Space ◽  
Image Resolution ◽  
Stereo Image ◽  
Digital Cameras ◽  
Dense Stereo Matching ◽  
Dense Matching ◽  
Optimization Stage ◽  
Image Pairs

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

ProPaCoL-Net: A Novel Recursive Stereo Image SR Network with Progressive Parallax Coherency Learning

2020 ◽  
Vol 2020 (14) ◽  
pp. 342-1-342-8
Author(s):  
Jeonghun Kim ◽  
Munchurl Kim
Keyword(s):  
Autonomous Vehicles ◽  
Stereo Matching ◽  
Low Cost ◽  
Super Resolution ◽  
Stereo Image ◽  
Stereo Images ◽  
Computationally Efficient ◽  
Stereo Cameras ◽  
Left Image ◽  
Coherency Loss

Recently, stereo cameras have been widely packed in smart phones and autonomous vehicles thanks to low cost and smallsized packages. Nevertheless, acquiring high resolution (HR) stereo images is still a challenging problem. While the traditional stereo image processing tasks have mainly focused on stereo matching, stereo super-resolution (SR) has drawn less attention which is necessitated for HR images. Some deep learning based stereo image SR works have recently shown promising results. However, they have not fully exploited binocular parallax in SR, which may lead to unrealistic visual perception. In this paper, we present a novel and computationally efficient convolutional neural network (CNN) based deep SR network for stereo images by learning parallax coherency between the left and right SR images, which is called ProPaCoL-Net. The proposed ProPaCoL-Net progressively learns parallax coherency via a novel recursive parallax coherency (RPC) module with shared parameters. The RPC module is effectively designed to extract parallax information in prior for the left image SR from its right view input images and vice versa. Furthermore, we propose a parallax coherency loss to reliably train the ProPaCoL-Net. From extensive experiments, the ProPaCoL-Net shows to outperform the very recent state-of-the-art method with average 1.15 dB higher in PSNR.

A comparison of the cost of research in South Africa's public research and higher education institutions

2008 ◽  
Vol 104 (11/12) ◽  
Author(s):  
D.R. Walwyn
Keyword(s):  
Higher Education ◽  
Higher Education Institutions ◽  
Public Research ◽  
Pricing Policy ◽  
The Public ◽  
Annual Salary ◽  
Research Sector ◽  
Salary Survey ◽  
The Cost ◽  
Comparative Information

Despite the importance of labour and overhead costs to both funders and performers of research in South Africa, there is little published information on the remuneration structures for researchers, technician and research support staff. Moreover, there are widely different pricing practices and perceptions within the public research and higher education institutions, which in some cases do not reflect the underlying costs to the institution or the inherent value of the research. In this article, data from the 2004/5 Research and Development Survey have been used to generate comparative information on the cost of research in various performance sectors. It is shown that this cost is lowest in the higher education institutions, and highest in the business sector, although the differences in direct labour and overheads are not as large as may have been expected. The calculated cost of research is then compared with the gazetted rates for engineers, scientists and auditors performing work on behalf of the public sector, which in all cases are higher than the research sector. This analysis emphasizes the need within the public research and higher education institutions for the development of a common pricing policy and for an annual salary survey, in order to dispel some of the myths around the relative costs of research, the relative levels of overhead ratios and the apparent disparity in remuneration levels.

The Internet Trap

2018 ◽  
Author(s):  
Matthew Hindman
Keyword(s):  
The Internet ◽  
Net Neutrality ◽  
The Public ◽  
Attention Economy ◽  
Digital News ◽  
The Cost ◽  
Over Time

The Internet was supposed to fragment audiences and make media monopolies impossible. Instead, behemoths like Google and Facebook now dominate the time we spend online—and grab all the profits from the attention economy. This book explains how this happened. It sheds light on the stunning rise of the digital giants and the online struggles of nearly everyone else—and reveals what small players can do to survive in a game that is rigged against them. The book shows how seemingly tiny advantages in attracting users can snowball over time. The Internet has not reduced the cost of reaching audiences—it has merely shifted who pays and how. Challenging some of the most enduring myths of digital life, the book explains why the Internet is not the postindustrial technology that has been sold to the public, how it has become mathematically impossible for grad students in a garage to beat Google, and why net neutrality alone is no guarantee of an open Internet. It also explains why the challenges for local digital news outlets and other small players are worse than they appear and demonstrates what it really takes to grow a digital audience and stay alive in today's online economy. The book shows why, even on the Internet, there is still no such thing as a free audience.

Strategy for Protecting the North Sea: A Chemical Industry Perspective

1991 ◽  
Vol 24 (10) ◽  
pp. 269-276
Author(s):  
J. R. Lawrence ◽  
N. C. D. Craig
Keyword(s):  
North Sea ◽  
Chemical Industry ◽  
Important Research ◽  
The Public ◽  
The North ◽  
Anthropogenic Inputs ◽  
Seven Principles ◽  
The North Sea ◽  
The Cost

The public has ever-rising expectations for the environmental quality of the North Sea and hence of everreducing anthropogenic inputs; by implication society must be willing to accept the cost of reduced contamination. The chemical industry accepts that it has an important part to play in meeting these expectations, but it is essential that proper scientific consideration is given to the potential transfer of contamination from one medium to another before changes are made. A strategy for North Sea protection is put forward as a set of seven principles that must govern the management decisions that are made. Some areas of uncertainty are identified as important research targets. It is concluded that although there have been many improvements over the last two decades, there is more to be done. A systematic and less emotive approach is required to continue the improvement process.

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