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Intelligent Systems Reference Library 182 Margarita N Favorskaya Lakhmi C Jain Editors Computer Vision in Control Systems—6 Advances in Practical Applications Intelligent Systems Reference Library Volume 182 Series Editors Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland Lakhmi C Jain, Faculty of Engineering and Information Technology, Centre for Artiﬁcial Intelligence, University of Technology, Sydney, NSW, Australia; KES International, Shoreham-by-Sea, UK; Liverpool Hope University, Liverpool, UK The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias It contains well integrated knowledge and current information in the ﬁeld of Intelligent Systems The series covers the theory, applications, and design methods of Intelligent Systems Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included The list of topics spans all the areas of modern intelligent systems such as: Ambient intelligence, Computational intelligence, Social intelligence, Computational neuroscience, Artiﬁcial life, Virtual society, Cognitive systems, DNA and immunity-based systems, e-Learning and teaching, Human-centred computing and Machine ethics, Intelligent control, Intelligent data analysis, Knowledge-based paradigms, Knowledge management, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender systems, Robotics and Mechatronics including human-machine teaming, Self-organizing and adaptive systems, Soft computing including Neural systems, Fuzzy systems, Evolutionary computing and the Fusion of these paradigms, Perception and Vision, Web intelligence and Multimedia ** Indexing: The books of this series are submitted to ISI Web of Science, SCOPUS, DBLP and Springerlink More information about this series at http://www.springer.com/series/8578 Margarita N Favorskaya Lakhmi C Jain • Editors Computer Vision in Control Systems—6 Advances in Practical Applications 123 Editors Margarita N Favorskaya Reshetnev Siberian State University of Science and Technology Krasnoyarsk, Russia Lakhmi C Jain Faculty of Engineering and Information Technology, Technology Centre for Artiﬁcial Intelligence University of Technology Sydney Broadway, NSW, Australia ISSN 1868-4394 ISSN 1868-4408 (electronic) Intelligent Systems Reference Library ISBN 978-3-030-39176-8 ISBN 978-3-030-39177-5 (eBook) https://doi.org/10.1007/978-3-030-39177-5 © Springer Nature Switzerland AG 2020 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface The research book is a continuation of our previous books which are focused on the recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms • Computer Vision in Control Systems—1, Mathematical Theory, ISRL Series, Volume 73, Springer-Verlag, 2015 • Computer Vision in Control Systems—2, Innovations in Practice, ISRL Series, Volume 75, Springer-Verlag, 2015 • Computer Vision in Control Systems—3, Aerial and Satellite Image Processing, ISRL Series, Volume 135, Springer-Verlag, 2018 • Computer Vision in Control Systems—4, Real Life Applications, ISRL Series, Volume 136, Springer-Verlag, 2018 • Computer Vision in Control Systems—5, Advanced Decisions in Technical and Medical Applications, ISRL Series, Volume 175, Springer-Verlag, 2020 The main aim of this volume is to present a sample of recent practical application of computer vision systems implemented by a number of researchers in Russian Federation The book is directed to the Ph.D students, professors, researchers, and software developers working in the ﬁeld of computer vision technologies and their applications We wish to express our gratitude to the authors and reviewers for their contributions The assistance provided by Springer-Verlag is acknowledged Krasnoyarsk, Russia Broadway, Australia Margarita N Favorskaya Lakhmi C Jain v Contents Image Processing for Practical Applications Lakhmi C Jain and Margarita N Favorskaya 1.1 Introduction 1.2 Chapters in the Book 1.3 Conclusions References 5 New Methods of Forming and Measurement of Sub-pixel Shift of Digital Images Yuriy S Radchenko and Olga A Masharova 2.1 Introduction 2.2 Shift Algorithm Based on Discrete Chebyshev Transformation 2.3 Position Estimation in Noisy Images 2.4 Analyze of Autocorrelation Function 2.5 Shift’s Estimation by Using Discriminator 2.5.1 Discriminator Structure 2.5.2 Distribution Law of Estimation 2.5.3 Robust Estimate of Signal Parameter 2.6 Conclusions References The Characteristics of the Phase-Energy Image Spectrum Andrei V Bogoslovsky, Irina V Zhigulina, Vladimir A Sukharev and Maksim A Pantyukhin 3.1 Introduction 3.2 The Model of One-Dimensional Energy-Phase Spectrum 3.3 The Model of Two-Dimensional Phase-Energy Spectrum 3.4 Conclusions References 11 13 14 15 18 20 22 23 25 25 26 31 35 36 vii viii Contents 39 39 40 42 43 47 49 51 52 53 53 54 56 57 59 61 62 63 63 64 65 67 68 70 71 75 75 77 77 78 79 Detectors Fields Andrei V Bogoslovsky, Andrey V Ponomarev and Irina V Zhigulina 4.1 Introduction 4.2 The Primitive Detectors Field 4.3 Drift of the Detectors Field 4.4 Two-Dimensional Discrete Filtering of Detectors Fields for Output Signals 4.5 Experimental Studies 4.6 Using Detectors Field Filtering in Images Affected by Motion Blur 4.7 Conclusions References Comparative Evaluation of Algorithms for Trajectory Filtering Konstantin K Vasiliev and Oleg V Saverkin 5.1 Introduction 5.2 Target Motion Models 5.3 Trajectory Filtration Algorithms 5.4 Body-Fixed Frame 5.5 Comparative Analysis of Filtration Efﬁciency 5.6 Conclusions References Watermarking Models of Video Sequences Margarita N Favorskaya 6.1 Introduction 6.2 Related Work 6.3 Watermarking Model of Videos in Uncompressed Domain 6.4 Watermarking Models of Videos in Compressed Domain 6.4.1 Watermarking Schemes for Compressed Video Sequences 6.4.2 Watermarking Models for Three Strategies 6.5 Basic Requirements for Watermarking Schemes 6.6 Conclusions References Experimental Data Acquisition and Management Software for Camera Trap Data Studies Aleksandr Zotin and Andrey Pakhirka 7.1 Introduction 7.2 Related Work 7.3 Camera Traps Data Contents 7.4 Proposed Software System 7.4.1 Module of Data Management 7.4.2 Module of Preliminary Analysis 7.4.3 Module of Image Enhancement 7.4.4 Module of Animal Detection 7.4.5 Module of CNN Control 7.4.6 Module of Semantic Description 7.5 Conclusions References ix Two-Stage Method for Polyps Segmentation in Endoscopic Images Nataliia A Obukhova, Alexander A Motyko and Alexaner A Pozdeev 8.1 Introduction 8.2 Related Work 8.3 Proposed Two-Stage Approach for the Classiﬁcation and Segmentation of Polyps 8.3.1 The Idea of a Two-Stage Approach 8.3.2 Databases 8.3.3 Binary Classiﬁcation Based on Global Features 8.3.4 Segmentation Based on CNN 8.4 Experimental Studies 8.5 Conclusions References 81 82 83 85 86 87 88 90 91 93 93 94 Algorithms for Markers Detection on Facies Images of Human Biological Fluids in Medical Diagnostics Victor Krasheninnikov, Larisa Trubnikova, Anna Yashina, Marina Albutova and Olga Malenova 9.1 Introduction 9.2 The Examples of Images of Biological Liquids Facies 9.3 The Image Preprocessing 9.4 Algorithms for Markers Detection and Recognition 9.5 Statistical Tests of Algorithms 9.6 Conclusions References 96 96 98 98 100 101 104 105 107 108 109 110 117 123 124 124 10 An Investigation of Research Activities in Intelligent Data Processing Using Data Envelopment Analysis 127 Andrey V Lychev, Aleksei V Rozhnov and Igor A Lobanov 10.1 Introduction 128 10.2 The Foresight of Impending Smart Infrastructure from the Position of Pervasive Informatics 129 x Contents 10.3 Data Envelopment Analysis Background 10.4 System Integration of Research Activities in Geosocial Networking Using Data Envelopment Analysis 10.5 Conclusions References 131 134 136 137 11 Hybrid Optimization Modeling Framework for Research Activities in Intelligent Data Processing Aleksei V Rozhnov, Andrey V Lychev and Igor A Lobanov 11.1 Introduction 11.2 Intelligent Data Processing and Object-Based Image Analysis 11.3 Hybrid Optimization Modeling Framework 11.3.1 Functionality of Hybrid Optimization Modeling Framework 11.3.2 Experimental Studies 11.4 Conclusions References 141 142 143 146 12 Non-local Means Denoising Algorithm Based on Local Binary Patterns S K Kartsov, D Yu Kupriyanov, Yu A Polyakov and A N Zykov 12.1 Introduction 12.2 Related Work 12.3 Description of Non-local Means Algorithm 12.4 Modiﬁed Non-local Means Algorithm 12.5 Non-local Means Based on Local Binary Patterns 12.6 Experimental Studies 12.7 Conclusions References 13 The Object-Oriented Simultaneous Localization and Mapping on the Spherobot Platform Vladimir A Antipov, Vasilii P Kirnos, Vera A Kokovkina and Andrey L Priorov 13.1 Introduction 13.2 Robot Construction 13.3 Proposed Algorithm 13.3.1 Data Acquisition and Synchronization 13.3.2 Determining the Location of the Mobile Platform 13.3.3 Construction of Three-Dimensional Map 13.4 Results 13.5 Conclusions References 146 148 149 150 153 154 155 156 158 160 161 162 163 165 165 166 167 167 168 170 172 173 175 12 Non-local Means Denoising Algorithm Based on Local Binary … 161 where i c corresponds to the center pixel value (xc , yc ), i n corresponds to the values of eight adjacent pixels, and the function s(x) is defined by Eq 12.7 s(x) = if x ≥ 0 otherwise (12.7) The distribution of LBP codes is used as a classification or segmentation for further texture analysis 12.6 Experimental Studies Let us consider a fragment of the scanned document (Fig 12.5) We will perform noise reduction of this document, using the original NLM algorithm and method based on the proposed approach, which uses LBP operator All measurements were carried out, using MatLab 7.5.0 system, on a computer with Intel (R) i3-3220 3.30 GHz processor and GB of installed memory For obtaining results by the standard method, a code, that implements the standard method in MatLab [26], was used The results, that were obtained during the work, are presented in Fig 12.6 Using of scanned old documents, containing the traces of noise, as source images does not allow the application of various metrics for assessment a quality of the selected algorithms But, as seen from Fig 12.6, a visual perception of image, obtained by the proposed approach, is better, than by the original algorithm The speed of operation of these algorithms, in this study, is, in principle, the same and depends from the size of the selected image areas R and r and the image size During the execution of both algorithms, the difference between areas of size r around pixel p and all pixels q within region R is calculated As a result, the complexity of the calculation on the image of size M × N makes M * N * R2 * r comparisons for Fig 12.5 Original noisy image 162 S K Kartsov et al Fig 12.6 Images processed by: a original NLM algorithm, b NLM algorithm based on LBP calculating new values for all pixels of the image This suggests about a comparable speed of operation of both algorithms while improving the noise reduction process by the proposed method The calculation of the area around each pixel p by LBP operator in the proposed method does not significantly affect to the final execution time At the same time, as mentioned above, it can even be reduced However, for a significant acceleration of operation of the proposed method, the optimal ways for the assignment of weights between blocks by various methods can be used, for example [4–14] 12.7 Conclusions The objective of this study is a quality improvement of the resulting image during the noise reduction operation The application of the proposed approach allows one to form an averaged final value of image pixels based on calculations not only within the region R, but also using values of all pixels p, having a similar structure within the region r around themselves within the entire image Based on all the above, it can be concluded, that using this approach for improvement the quality of the resulting image in the process of noise reduction of the image obtained by scanning a document, allowed to solve the already known task at a qualitatively new level, opening the new possibilities of the methods used in the algorithm Acknowledgements The reporting study was conducted in connection with the work on old paper documents, when converting them into electronic form by scanning, and the need for improvement the quality of the scanned documents 12 Non-local Means Denoising Algorithm Based on Local Binary … 163 References Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising In: IEEE International Conference on Computer Vision and Pattern Recognition, vol 2, pp 60–65 (2005) Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one Multiscale Model Simul 2, 490–530 (2005) Buades, A., Coll, B., Morel, J.M.: Non-local image and movie denoising Int J Comput Vis 2, 123–139 (2008) Hedjam, R., Moghaddam, R.F., Cheriet, M.: Markovian clustering for the non-local means image denoising In: 16th IEEE International Conference on Image Processing, pp 3877–3880 (2009) James, W., Stein, C.: Contributions to the theory of statistics Estimation with quadratic loss In: 4th Berkeley Symposium on Mathematical Statistics and Probability, vol 1, pp 361–379 (1961) Wu, Y., Tracey, B., Natarajan, P., Noonan, J.P.: James–Stein type center pixel weights for non-local means image denoising IEEE Signal Process Lett 20(4), 411–414 (2013) Lai, R., Dou, X.: Improved non-local means filtering In: 3rd International Congress on Image and Signal Processing, vol 2, pp 720–722 (2010) Khan, A., El-Sakka, M.R.: Non-local means using adaptive weight thresholding In: 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp 67–76 (2016) Mahmoudi, M., Sapiro, G.: Fast image and video denoising via nonlocal means of similar neighborhoods IEEE Signal Process Lett 12(12), 839–842 (2005) 10 Bilcu, R.C., Vehvilainen, M.: Combined non-local averaging and intersection of confidence intervals for image denoising In: 15th IEEE International Conference on Image Processing, pp 1736–1739 (2008) 11 Adams, A., Gelfand, N., Dolson, J., Levoy, M.: Gaussian KD-trees for fast high-dimensional filtering ACM Trans Graph 28, 21.1–21.12 (2009) 12 Orchard, J., Ebrahimi, M., Wong, A.: Efficient non-local-means denoising using the SVD In: Proceedings of IEEE International Conference on Image Processing, pp 1732–1735 (2008) 13 Coupe, P., Yger, P., Barillot, C.: Fast non-local means denoising for 3D MRI images In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 33–40 (2006) 14 Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing ACM Trans Graph 28, 24.1–24.8 (2009) 15 Enríquez, A.E.P., Ponomaryov, V.: Image denoising using block matching and discrete cosine transform with edge restoring In: International Conference on Electronics, Communications and Computers, pp 140–147 (2016) 16 Wang, J., Guo, Y., Ying, Y., Liu, Y., Peng, Q.: Fast non-local algorithm for image denoising In: IEEE International Conference on Image Processing, pp 1429–1432 (2006) 17 Zhong, H., Zhang, J., Liu, G.: Robust polarimetric SAR despeckling based on nonlocal means and distributed Lee filter IEEE Trans Geosci Remote Sens 52(7), 4198–4210 (2013) 18 Lee, J.S.: Digital image smoothing and the sigma filter Comput Vis Graph Image Process 24(2), 255–269 (1983) 19 Chan, C., Fulton, R., Feng, D.D., Meikle, S.: Median non-local means filtering for low SNR image denoising: application to pet with anatomical knowledge In: IEEE Nuclear Science Symposium & Medical Imaging Conference, pp 3613–3618 (2010) 20 Irrera, P., Bloch, I., Delplanque, M.: A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images Med Image Anal 28, 33–45 (2016) 21 Zhan, Y., Ding, M., Wu, L., Zhang, X.: Nonlocal means method using weight refining for despeckling of ultrasound images Signal Process 103, 201–213 (2014) 22 Xu, J., Hu, J., Jia, X.: A multistaged automatic restoration of noisy microscopy cell images IEEE J Biomed Health Inform 19(1), 367–376 (2015) 164 S K Kartsov et al 23 Genin, L., Champagnat, F., Besnerais, G.L., Coret, L.: Point object detection using a NL-means type filter In: 18th IEEE International Conference on Image Processing, pp 3533–3536 (2011) 24 Kim, M., Park, D., Han, D.K., Ko, H.: A novel approach for denoising and enhancement of extremely low-light video IEEE Trans Consum Electron 61(1), 72–80 (2015) 25 Barnsley, M., Hurd, L.: Fractal Image Compression A K Peters Ltd., Wellesley, MA (1993) 26 Ojala, T., Pietikainen, M., Maenpaa, T.: Multi resolution gray-scale and rotation invariant texture classification with local binary patterns IEEE Trans Pattern Anal Mach Intell 24(7), 971–987 (2002) Chapter 13 The Object-Oriented Simultaneous Localization and Mapping on the Spherobot Platform Vladimir A Antipov, Vasilii P Kirnos, Vera A Kokovkina and Andrey L Priorov Abstract The chapter is about the extraordinary robot, which looks like a ball and is controlled by Wi-Fi communication It has the microcomputer, camera with special lens, and microcontroller as a slot for additional sensors On this platform, an algorithm of the simultaneous localization and mapping is running The algorithm builds the path of the robot and map of the environment The path of robot is already noisy To overcome a noise influence, we add encoders, which describe the passable way of the robot Keywords SLAM · Landmarks detectors · Landmarks descriptors · Wheel encoders · Robotic kinematic model 13.1 Introduction Mobile robotics systems have broad application in the most different areas of modern life: in the industry, different military and rescue applications, in medicine, etc Each of these applications has different requirements to characteristics of robots There are a number of tasks, for which execution the mobile robot should have rather small sizes The robots described further in the chapter it supposed to use for a research of rooms and areas, where no access has for people due to a danger for life This robot also solves the problem of penetration because person cannot get there physically, for example in the narrow horizontal mines In addition, it is offered to use robots as supportive application to performing diagnostics of cases of the produced products in hardly accessible compartments at faults by airplanes or river transport Extraordinary construction of this robot comes across of its size The map is a rather convenient tool for determining the position in space, graphical image of the terrain plan, or navigation Map rather has disadvantages, so it is rarely used in the navigation of mobile robots without additional instrumentation, which V A Antipov · V P Kirnos · V A Kokovkina · A L Priorov (B) P.G Demidov Yaroslavl State University, 14, Sovetskaya St., Yaroslavl 150003, Russian Federation V P Kirnos e-mail: v.kirnos@uniyar.ac.ru © Springer Nature Switzerland AG 2020 M N Favorskaya and L C Jain (eds.), Computer Vision in Control Systems—6, Intelligent Systems Reference Library 182, https://doi.org/10.1007/978-3-030-39177-5_13 165 166 V A Antipov et al are the environment sensors These two tools are complementary: the contribution of maps to the assessment of the current location in space increases with decreasing accuracy and quality of sensors of perception of space, and otherwise The chapter focuses on the comparison of different approaches to the solutions of these subproblems Section 13.2 is about a mobile robotics platform named spherobot and its applications Section 13.3 describes the proposed algorithm based on the extended Kalman filter because, hereinafter, this information is required more than once [1, 2] Here, we describe how to reconstruct 3D scene using a fisheye camera [3–10] Results are presented in Sect 13.4, while Sect 13.5 concludes the chapter 13.2 Robot Construction The mobile robot needs the locomotion mechanisms, which allow it to move without restrictions during the entire environment There are many options of possible ways of movement Therefore, a choice of a way of movement is an important aspect of construction of mobile robots The majority of these mechanisms of locomotion are inspired by their biological analogs though not without any exception The wheel is purely human invention that provides extremely high performance on a plan surface This mechanism is not completely alien to biological systems (for example, the biaxial system of walking can be approximated by a polygon with the parties equal to step length) However, the nature did not create completely rotating actively working connection, which is necessary for a wheel movement Almost there are problems with complexity of locomotion biological mechanisms and mechanisms of providing them with energy, expendables, and individual production of each component Mobile robots usually move ether with use of wheel mechanisms or with use of a small amount of hinged legs, which is the simplest of biological approaches to locomotion The environment created by the person often consists of the designed smooth surfaces Thus, use of wheel robots in similar conditions is most effective in spite of this approach has own features and restrictions The robot described in the chapter carries out movement by principles of the mechanics inherent two-wheeled robots, whose center of gravity lies below an axis of wheels This solution helps the robot to save from the balancing problem, but causes to write the special software Also it is required to add additional sensors and optionally more powerful power source or more powerful transmitter of a signal (if data processing is made for balancing of the robot by the external server) That, first, makes heavier the robot, second, occupies already a small amount of internal volume of the robot A priority was including the small size and a certain distribution of the robot mass, these parameters was important to be considered The mobile robot consists of two hemispheres serving as wheels and providing movement and the central part remaining at the movement motionless because of the gravity center, which is displaced down The central part also serves for placement of the robot hardware: control board, communication module, sensors, camera, etc 13 The Object-Oriented Simultaneous Localization and Mapping … 167 Fig 13.1 Construction of the spherobot The device of the robot allows it to rise always in the correct situation even if it lies on one side The robots construction is presented on Fig 13.1 The communication is the basic part of this robot Rather there are problems with limitations of battery source that is why the solution is calculated outsource of the robot on the server Wi-Fi is used for the communication between the server and robot By the wireless protocol, the robot streams the video data and data from encoders to the server The sensors data broker on the robot is the Raspberry Pi Zero W 13.3 Proposed Algorithm The robot is used fisheye lens with 260° on the camera for the getting much information as possible form the one camera The axis of the camera is directed vertically up It gives us the image of a ceiling and the image around the robot We use the camera for constructing a three-dimensional point cloud (resolution 640 × 480) It is fixed in such a way that it is possible to obtain panoramic images The algorithm consists of three main stages Data acquisition and synchronization is provided in Sect 13.3.1 Determining the location of the mobile platform is given in Sect 13.3.2 Construction of 3D map is discussed in Sect 13.3.3 13.3.1 Data Acquisition and Synchronization The mobile platform is controlled remotely by a human operator The software of the robot takes off data from the sensors to the file “rosbag” Then, the obtained file is processed by an algorithm implemented on ROS Since the sensors have a different reading frequency, it is necessary to synchronize the received data in time A small period of time is taken, and if all the sensors are activated at this moment, then the data is processed 168 V A Antipov et al 13.3.2 Determining the Location of the Mobile Platform Simultaneous Localization And Mapping (SLAM) algorithm is used to determine the location of the mobile platform This chapter uses Extended Kalman Filter SLAM (EKF-SLAM) algorithm, which considers the state of the system as Gaussian distribution and constantly evaluates the mathematical expectation and covariance matrix The update of the system state evaluation is carried out in two stages: prediction and correction [3] Prediction stage: (1) Prediction of the system state is provided by Eq 13.1 μt = g(μt−1 , u t ) (13.1) (2) Prediction of covariance error is estimated by Eq 13.2, where μt is the prediction of the system state at the current moment of time, g(μt−1 , u t ) is the prediction function of the system state, u t is the control action at the current moment of time, Σt is the prediction of the system state error at the current moment of time, G t is the state transition matrix, Rt is the system noise Σt = G t ∗ Σt−1 ∗ G tT + Rt (13.2) Equation 13.3 is used to define Rt : Rt = Vt Σc VtT , (13.3) where Vt is the matrix of state transition of control action, Σc is the covariance of the control action Correction stage: (1) The calculation of Kalman gain is implemented by Eq 13.4 K t = Σt ∗ HtT ∗ Ht ∗ Σt ∗ HtT + Q −1 (13.4) (2) Update the estimate with the measurement of z t has a view of Eq 13.5 μt = μt + K t ∗ (z t − h(μt )) (13.5) (3) Update the covariance error is provided by Eq 13.6, where K t is Kalman gain, Ht is the measurement matrix showing the ratio of measurements and states, Q is the covariance of measurement noise, z t is the measurement at the current moment of time, I is the identity matrix Σt = (I − K t ∗ Ht ) ∗ Σt (13.6) 13 The Object-Oriented Simultaneous Localization and Mapping … 169 After filtering, the local maximum of the output normed filter signal module is allocated in each received fragment The association of data does not require using the criterion of close locating the observed landmark to the predicted location (geometric method) but aided by the technical vision Many perceptual techniques, such as vision, provide a lot of information about shape, color, and texture, and all of this can be used to find a correspondence between two sets of landmarks The main steps of the data association algorithm are the following: Step Converting the fisheye images into panoramic image Step Searching for the area of the image, where found landmark is located Step Searching the key points and their descriptors by Scale-Invariant Feature Transform (SIFT) method in the found image area (Fig 13.2) Step Comparison of landmarks on a set of coinciding key points The main steps of the algorithm for constructing a panoramic image are following: Step Determination of the center and the inner and outer radii Determination of the center can be automated using Hough transformation for the search of circles Step Building a map for converting fisheye image into a panoramic image This map is a display of the location of individual pixels of a fisheye image in a panoramic image (Eqs 13.7–13.10): r= x f = xc + r ∗ sin(θ ), (13.7) y f = yc + r ∗ cos(θ ), (13.8) yp (Router − Rinner ) + Rinner , height Fig 13.2 Description of the landmark using SIFT-descriptors (13.9) 170 V A Antipov et al Fig 13.3 Converting a fisheye image to a panoramic image θ = 2π xp width (13.10) Step Use the map of conversion with the application of interpolation (Fig 13.3) 13.3.3 Construction of Three-Dimensional Map The displacement map is not obtained using two cameras, but one camera for two consecutive images from different viewpoints Each viewpoint is defined using EKFSLAM algorithm In this chapter, a local stereo matching algorithm is used, in which the displacement map is determined based on the comparison of pixel windows on the epipolar line using the sum of absolute differences (Eq 13.11): l k [B(x + i, y + j) − M(x + d + i, y + j)]2 , E S AD ( p, d) = (13.11) i=−1 j=−k where B is the first image, M is the second one [5, 6] The accuracy of the estimation of the displacement map often suffers from extreme scenarios, such as an area without texture, overexposure, repeated structure, etc Therefore, it is necessary a post-processing to improve the accuracy of the displacement map During post-processing stage, Weighted Least Squares (WLS) filtering is used because it provides good smoothing that preserve the edges [4] The purpose of filtering the stereo correspondence can be expressed as minimizing Eq 13.12: 13 The Object-Oriented Simultaneous Localization and Mapping … 171 Fig 13.4 Example of WLS-filtering: a raw image, b image after filtration Dp − Dp + λ ax, p (I ) p ∂D ∂x + a y, p (I ) p ∂D ∂y , (13.12) p where D is the original image, D is the filtered image, p is the index that determines the pixel position, ax, p (I ) and a y, p (I ) are the weighting coefficients, which are defined by Eqs 13.13–13.14: ax, p (I ) = ∂l ( p) ∂x α a y, p (I ) = ∂l ( p) ∂y α −1 +ε , (13.13) , (13.14) −1 +ε where l is the brightness channel at logarithmic scale I , parameter α determines the sharpness of the border, ε is the constant with small value (Fig 13.4) The fisheye camera model is based on a spherical projection Suppose there is a sphere with a unit radius and a point P in space, as shown in Fig 13.5 Point P is the projection of point P on the sphere, i.e a point P is the intersection of the surface of a sphere with a line drawn from the center of the sphere O to the point P [5] Thus, the displaying between space points and points on the surface of a sphere is determined Then, these points are vertically projected onto the image plane, a resulting circular image is shown in Fig 13.3 Position of the point P is defined by Eq 13.15 ⎡ ⎤ ⎡ ⎤ x r ∗ sinφ ∗ cosφ P = ⎣ y ⎦ = ⎣ r ∗ sinφ ∗ sinφ ⎦ x r ∗ cosθ (13.15) 172 V A Antipov et al Fig 13.5 Fisheye camera model Knowing the coordinates of the point P and the displacement map, it is possible to determine the coordinates of the point P (Eqs 13.16–13.17) [6]: P = λ(θ, φ)P , (13.16) λ(θ, φ) = b ∗ f /d(θ, φ), (13.17) where b is the distance between camera viewpoints, d is the displacement map, f is the focal length 13.4 Results To evaluate the operation of EKF-SLAM algorithm, the modeling environment “gazebo” is used On the map “willow garage”, two arrivals are held In each race, the standard deviation was calculated Results of researches are shown in Figs 13.6, 13.7 and Table 13.1 Table 13.1 shows that the developed algorithm of simultaneous localization and mapping works approximately like other SLAM algorithms The research algorithm has a root-mean-square error in the range from 0.01 to 0.03 m, whereas Large-Scale Direct monocular (LCD)-SLAM has from 0.02 to 0.38 m and Red Green Blue Depth (RGBD)-SLAM has from 0.01 to 0.9 m However, the implemented algorithm has qualitative change in that it allows to obtain both two-dimensional and three-dimensional maps 13 The Object-Oriented Simultaneous Localization and Mapping … 173 Fig 13.6 Two arrivals in “willow garage”: a, c received maps, b, d its trajectories of the mobile platform on the map 13.5 Conclusions We presented the mobile platform and some technical problems, which we come across on the implementation stage The main goal of the chapter is to implement SLAM algorithm based on fisheye The key idea of this solution is to reconstruct 3D scene from only one camera with special lens The resulting algorithm is not worse than LCD-SLAM and RGBD-SLAM 174 V A Antipov et al Fig 13.7 Result of SLAM: a resulting map, b point cloud, c trajectory of the mobile platform in the room Table 13.1 Standard deviations of coordinates for each trajectory Trajectory Trajectory SD of coordinate X 0.281 0.014 SD of coordinate Y 0.096 0.04 SD of coordinate Y 0.032 0.02 13 The Object-Oriented Simultaneous Localization and Mapping … 175 References Newman, P.M.: EKF Based Navigation and SLAM Background Material, Notes and Example Code SLAM Summer School, Oxford (2006) Bailey, P., Beckler, M., Hoglund, R., Saxton, J.: 2D simultaneous localization and mapping Available at: https://pdfs.semanticscholar.org/82c6/ f386767d992b9edd2f56490245e30c80d6da.pdf Accessed 12 Sept 2019 Furman, Ya.A., Krevetskii, A.V., Peredereev, A.K., Rozhentsov, A.A., Khafizov, R.G., Egoshina, I.L., Leukhin, A.N.: Introduction to Contour Analysis Fizmatlit, Moscow (in Russian) (2003) Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multiscale tone and detail manipulation J ACM Trans Graph 27(30), 67.1–67.10 (2008) Song, M., Watanabe, H.: Robust 3D reconstruction with omni-directional camera based on structure from motion In: International Workshop on Advanced Image Technology, pp 1–4 (2018) Aliaga, D., Yanovsky, D., Carlbom, I.: A dense sampling approach for rendering large indoor environments Comput Graph Appl 22–30 (2003) Special Issue on 3D Reconstruction and Visualization Fleck, S., Busch, F., Biber, P., Straber, W.: Omnidirectional 3D modeling on a mobile robot using graph cuts In: 2005 IEEE International Conference on Robotics and Automation, pp 1748– 1754 (2005) Igbinedion, I., Han, H.: 3D stereo reconstruction using multiple spherical views Available at: https://web.stanford.edu/class/ee368/Project_Autumn_1516/Reports/Igbinedion_Han.pdf Accessed 10 Sept 2019 Hahnel, D., Schulz, D., Burgard, W.: Mobile robot mapping in populated environments Adv Robot 17(7), 579–597 (2003) 10 Negenborn, R.: Robot localization and Kalman filters On finding your position in a noisy world Thesis number (for the degree of Master of Science): INF/SCR-03-09, Utrecht University (2003) ... neuroscience, Artiﬁcial life, Virtual society, Cognitive systems, DNA and immunity-based systems, e-Learning and teaching, Human-centred computing and Machine ethics, Intelligent control, Intelligent... data analysis, Knowledge-based paradigms, Knowledge management, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender... the 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Budapest, Hungary (in print) (2019) 10 Krasheninnikov, V.R ., Yashina, A.S ., Malenova, O.E.:
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