This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume,Introduction to Autonomous Mobile Robotscan serve as a textbook or a working tool for beginning practitioners.
Introduction To Autonomous Mobile Robots 2nd Edition Pdf Download
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Course Autonomous Mobile RobotsBachelor Artificial IntelligenceThis is the information of Fall 2016That year the course was given by Arnoud Visser. The course is still given in 2021-2022, with Herke van Hoof and Shaodi You as lecturers. The information of the year 2015 is also still available.DescriptionThe description is available in the course catalogue with code AUMR6Y. The course is a free choice in the Bachelor Artificial Intelligence Curriculum. Contents This course gives an introduction in the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The focus will be on the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks. It synthesizes material from the fields of kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence and probability theory.This course is based on the book 'Introduction to Autonomous Mobile Robots', from Prof. Dr. Roland Yves Siegwart, Prof. Dr. Illah R. Nourbakhsh and Prof. Dr. Davide Scaramuzza. The assignments are all based on the Matlab environment.This YouTube lectures give a short introduction to the Matlab environment:the workspace, variables, vectors, colon operator, matrices, concatenating, matrix initialization. ScheduleThe official schedule should be found at mytimetable or datanose.The Studio Class Room is scheduled on Thursday and Friday, from 9u00 to 13u00 (first half). The schedule in the second half is condensed to two weeks (Monday, Tuesday and Thursday). The Studio Class Room will be a combination of lectures, book exercises and assignments. Chapter 1-4 of the book will be introduced by Toto van Inge. Chapter 5-6 will be covered by Arnoud Visser.Students, who were not able to attend a lecture, can catch up by listing to the recordings of my lectures (in Dutch). Download Lecturnity Player and listen to lecture, synchronized with the sheets.For the assignments not only a solution is expected, but also a rational. The experiments performed to solve the given problem should be described in a lab report, which will be graded based on the following criteria.Week 44: Chapter 1 & 2 - Introduction & Locomotion Solve OpenLoop steering assignment, including RWTH Toolbox Installation Instructions.Week 44: Chapter 3 - Kinematics Week 45: Chapter 4.1 Sensors for Mobile RobotsWeek 45: Chapter 4.2 Fundamentals of Computer Vision   SolveAssignment 3, matlab files, camera snapshots.Week 46: Chapter 4.3-4.5 Feature ExtractionWeek 46: Chapter 4.6-4.7 Place Recognition Week 47: Partial Exam Week 48: Chapter 5.1-5.5 The Challenge of Localization including YouTube lecture Solve Localization assignment, Matlab code, color picker and two locally recorded datasets (one for training and one for testing)Extra: 5 december recordings: (training set and testing set) and the 2014 Dataset from Areg.Week 48: Chapter 5.6 Probabilistic Map Based Localization Week 49: Chapter 5.6.8 Kalman Filter Localization including YouTube lecture and Kalman Filter Geometric Approach (Slides and Dutch recording) Only slides 9-45.Note the slightly different notation for the intermediate prediction; x(k+1k) by Choset et al.and x(t) by Thrun/Siegwart et al.Week 49: Chapter 5.8 Simultaneous Localization and Mapping (part I and part II)YouTube lecture EKF-SLAM, YouTube lecture Monocular SLAM SolveAssignment 4, with provided Logger, Example log, Matlab files and a locally recorded dataset (dataset and route (displacements of 15cm at the straight lines)).Week 50: Chapter 6 - Planning and Navigation (part I and part 2) YouTube lecture 1, lecture 2, lecture 3, lecture 4, lecture 5. Week 51: Partial Exam, December 20th, 9:00-11:00, C1.110 LiteratureRoland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza 'Introduction to Autonomous Mobile Robots', 2nd edition, The MIT Press, 2011.Reading guideMonday October 26th: page 56Tuesday October 27th: page 99Tuesday November 2th: page 194Tuesday November 9th: page 264Wednesday November 25th: page 321Wednesday December 2th: page 368Wednesday December 9th: page 424Embedding in AI curriculumThis course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 3rd edition, by Stuart Russell and Peter Norvig:Chapter 13: Quantifying UncertaintyChapter 14: Probabilistic ReasoningChapter 15: Probabilistic Reasoning over TimeChapter 24: PerceptionChapter 25: RoboticsEvaluationThe course is 2016 evaluated by the participants with a 5.0:. LinksThe Book's webpage.The Book's Slides/Exercices page.Davide Scaramuzza's Teaching site.Eldgenössische Technische Hochschule Zürich: Vision Algorithms for Mobile Robotics (2021) by Manasi Muglikar, Nico Messikommer and Davide Scaramuzza.University of Edinburgh: Introduction to Mobile Robotics (2023) - Chris Lu, Mobile Robotics, Springer, 2003 and Probabilistic Robotics, MIT Press 2005.University of Edinburgh: Advanced Robotics (2023) - Ram Ramamoorthy and Steve Tonneau, Modern Robotics, Cambridge Press, 2017 and Introduction to Robotics, Mechanics and Control Pearson 2018.University of Edinburgh: Introduction to Vision and Robotics (2021) - Mohsen Khadem, Robotics, Modelling, Planning and Control, Springer, 2009. Australian National University: Robotics (2023) - based on Mark W. Spong, Seth Hutchinson and M. Vidyasager, Robot Modelling and Control, Wiley, 2020.Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2017) by Roland Siegwart, Margarita Chli and Martin Rufli.Tecnico Lisboa: Introduction to Robotics (2018), Pedro Lima.Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2016) by Roland Siegwart, Margarita Chli and Martin Rufli.Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2015) by Roland Siegwart, Marco Huttler, Mike Bosse, Martin Rufli and Davide Scaramuzza.Davide Scaramuzza's old Teaching site (until 2014).Eldgenössische Technische Hochschule Zürich: Autonomous Mobile Robotics (2012) by Roland Siegwart, Margarita Chli, Martin Rufli and Davide Scaramuzza.Princeton University: Autonomous Robot Navigation (2015) by Dr. Christopher Clark.University of Edinburgh, Intelligent Autonomous Robotics (2016) by Prof. Barbara Webb.Università di Roma La Sapienza: Autonomous and Mobile Robotics (2016) by Prof. Giuseppe Oriolo.Southern Illinois University: Autonomous and Mobile Robotics (2013) by Dr. Henry Hexmoor.Princeton University: Autonomous Robot Navigation (2012) by Dr. Christopher Clark.Southern Illinois University: Autonomous and Mobile Robotics (2012) by Dr. Henry Hexmoor.Washington University in St. Louis: Mobile Robotics (2015) by David V. Lu.Carnegie Mellon University: Introduction to Robotics (2016) by Howie ChosetCarnegie Mellon University: Introduction to Robotics Programming (2007) by Alonzo KellyCarnegie Mellon University: Introduction to Mobile Robotics (2005) by Alonzo KellyCarnegie Mellon University: Introduction to Mobile Robotics (1997) by Illah R. NourbakhshSoftware toolkitsChapter 4, section 2.6 (page 186) - Structure from Motion:Structure from Motion toolbox by V. Raboud.Matlab Functions for Multiple View Geometry by A. Zisserman.Structure from Motion Toolkit by P. Torr.Matlab code for Non-Rigid Structure from Motion using Factorisation by L. Torresani.Chapter 4, section 5 (page 234) - Interest Point Detectors:Intel's OpenCV library, with e.g. Harris, MSER, FAST, SURF, ... Library maintained by Willow Garage.SUSAN.FAST Corner Detection.SIFT by D. Lowe.reimplementation in C and Matlab of SIFT, MSER, e.a. by A. Vedaldi.3D object recognition toolkit by Autonomous Systems Lab at ETH Zurich.GPU implementation SURF by Vision Lab at ETH Zurich.Chapter 5, section 8 (page 365) - Simultaneous Localization and Mapping algorithms:OpenSLAM, a list of SLAM software, list maintained by C. Stachniss.real-time monocular visual SLAM by Davison.real-time monocular visual SLAM algorithm PTAM by Klein and Murray.Matlab EKF SLAM simulator.RawSeeds collections of benchmarked datasets used for SLAM.Bibliography (page 444) - Referenced webpages:CVonline: On-line Compendium of Computer Vision, maintained by R.B. Fisher.The Intel Image Processing Library / Integrated Performance Primitives (Intel IPP)CMvision source codeFor proboticsIntel's OpenCV library, maintained by Willow Garage.Passive walking.Passive walking, the Corner Ranger.Computer Vision industryCamera Calibration Toolbox for MatlabList of camera calibration softwareOmnidirectional camera calibration toolbox from Christopher Mei.Omnidirectional camera calibration toolbox from Joao Barreto.Omnidirectional camera calibration toolbox from Davide Scaramuzza.OpenSLAM, a list of SLAM software, list maintained by C. Stachniss.Open source software for multi-view structure from motion.Photo Tourism.Voodoo Camera Tracker: A tool for integration of virtual and real scenes.Augmented-reality toolkit (ARToolkit).Parallel Tracking and Mapping (PTAM).Last updated November 11, 2022This web-page and the list of participants to this course is maintained by Arnoud Visser(a.visser@uva.nl)Facultyof ScienceUniversity of Amsterdam 2ff7e9595c
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