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Siegwart, Roland [Author]; Nourbakhsh, Illah Reza [Author]; Scaramuzza, Davide [Author]

Introduction to autonomous mobile robots - [2nd edition]

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  • Media type: Book
  • Title: Introduction to autonomous mobile robots
  • Contains: Machine generated contents note: 1. Introduction -- 1.1. Introduction -- 1.2. An Overview of the Book -- 2. Locomotion -- 2.1. Introduction -- 2.1.1. Key issues for locomotion -- 2.2. Legged Mobile Robots -- 2.2.1. Leg configurations and stability -- 2.2.2. Consideration of dynamics -- 2.2.3. Examples of legged robot locomotion -- 2.3. Wheeled Mobile Robots -- 2.3.1. Wheeled locomotion: The design space -- 2.3.2. Wheeled locomotion: Case studies -- 2.4. Aerial Mobile Robots -- 2.4.1. Introduction -- 2.4.2. Aircraft configurations -- 2.4.3. State of the art in autonomous VTOL -- 2.5. Problems -- 3. Mobile Robot Kinematics -- 3.1. Introduction -- 3.2. Kinematic Models and Constraints -- 3.2.1. Representing robot position -- 3.2.2. Forward kinematic models -- 3.2.3. Wheel kinematic constraints -- 3.2.4. Robot kinematic constraints -- 3.2.5. Examples: Robot kinematic models and constraints
    3.3. Mobile Robot Maneuverability -- 3.3.1. Degree of mobility -- 3.3.2. Degree of steerability -- 3.3.3. Robot maneuverability -- 3.4. Mobile Robot Workspace -- 3.4.1. Degrees of freedom -- 3.4.2. Holonomic robots -- 3.4.3. Path and trajectory considerations -- 3.5. Beyond Basic Kinematics -- 3.6. Motion Control (Kinematic Control) -- 3.6.1. Open loop control (trajectory-following) -- 3.6.2. Feedback control -- 3.7. Problems -- 4. Perception -- 4.1. Sensors for Mobile Robots -- 4.1.1. Sensor classification -- 4.1.2. Characterizing sensor performance -- 4.1.3. Representing uncertainty -- 4.1.4. Wheel/motor sensors -- 4.1.5. Heading sensors -- 4.1.6. Accelerometers -- 4.1.7. Inertial measurement unit (IMU) -- 4.1.8. Ground beacons -- 4.1.9. Active ranging -- 4.1.10. Motion/speed sensors -- 4.1.11. Vision sensors -- 4.2. Fundamentals of Computer Vision -- 4.2.1. Introduction -- 4.2.2. The digital camera -- 4.2.3. Image formation -- 4.2.4. Omnidirectional cameras
    4.2.5. Structure from stereo -- 4.2.6. Structure from motion -- 4.2.7. Motion and optical flow -- 4.2.8. Color tracking -- 4.3. Fundamentals of Image Processing -- 4.3.1. Image filtering -- 4.3.2. Edge detection -- 4.3.3. Computing image similarity -- 4.4. Feature Extraction -- 4.5. Image Feature Extraction: Interest Point Detectors -- 4.5.1. Introduction -- 4.5.2. Properties of the ideal feature detector -- 4.5.3. Corner detectors -- 4.5.4. Invariance to photometric and geometric changes -- 4.5.5. Blob detectors -- 4.6. Place Recognition -- 4.6.1. Introduction -- 4.6.2. From bag of features to visual words -- 4.6.3. Efficient location recognition by using an inverted file -- 4.6.4. Geometric verification for robust place recognition -- 4.6.5. Applications -- 4.6.6. Other image representations for place recognition -- 4.7. Feature Extraction Based on Range Data (Laser, Ultrasonic) -- 4.7.1. Line fitting -- 4.7.2. Six line-extraction algorithms
    4.7.3. Range histogram features -- 4.7.4. Extracting other geometric features -- 4.8. Problems -- 5. Mobile Robot Localization -- 5.1. Introduction -- 5.2. The Challenge of Localization: Noise and Aliasing -- 5.2.1. Sensor noise -- 5.2.2. Sensor aliasing -- 5.2.3. Effector noise -- 5.2.4. An error model for odometric position estimation -- 5.3. To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- 5.4. Belief Representation -- 5.4.1. Single-hypothesis belief -- 5.4.2. Multiple-hypothesis belief -- 5.5. Map Representation -- 5.5.1. Continuous representations -- 5.5.2. Decomposition strategies -- 5.5.3. State of the art: Current challenges in map representation -- 5.6. Probabilistic Map-Based Localization -- 5.6.1. Introduction -- 5.6.2. The robot localization problem -- 5.6.3. Basic concepts of probability theory -- 5.6.4. Terminology -- 5.6.5. The ingredients of probabilistic map-based localization
    5.6.6. Classification of localization problems -- 5.6.7. Markov localization -- 5.6.8. Kalman filter localization -- 5.7. Other Examples of Localization Systems -- 5.7.1. Landmark-based navigation -- 5.7.2. Globally unique localization -- 5.7.3. Positioning beacon systems -- 5.7.4. Route-based localization -- 5.8. Autonomous Map Building -- 5.8.1. Introduction -- 5.8.2. SLAM: The simultaneous localization and mapping problem -- 5.8.3. Mathematical definition of SLAM -- 5.8.4. Extended Kalman Filter (EKF) SLAM -- 5.8.5. Visual SLAM with a single camera -- 5.8.6. Discussion on EKF SLAM -- 5.8.7. Graph-based SLAM -- 5.8.8. Particle filter SLAM -- 5.8.9. Open challenges in SLAM -- 5.8.10. Open source SLAM software and other resources -- 5.9. Problems -- 6. Planning and Navigation -- 6.1. Introduction -- 6.2. Competences for Navigation: Planning and Reacting -- 6.3. Path Planning -- 6.3.1. Graph search -- 6.3.2. Potential field path planning
    6.4. Obstacle avoidance -- 6.4.1. Bug algorithm -- 6.4.2. Vector field histogram -- 6.4.3. The bubble band technique -- 6.4.4. Curvature velocity techniques -- 6.4.5. Dynamic window approaches -- 6.4.6. The Schlegel approach to obstacle avoidance -- 6.4.7. Nearness diagram -- 6.4.8. Gradient method -- 6.4.9. Adding dynamic constraints -- 6.4.10. Other approaches -- 6.4.11. Overview -- 6.5. Navigation Architectures -- 6.5.1. Modularity for code reuse and sharing -- 6.5.2. Control localization -- 6.5.3. Techniques for decomposition -- 6.5.4. Case studies: tiered robot architectures -- 6.6. Problems -- Bibliography -- Books -- Papers -- Referenced Webpages.
    Machine generated contents note:1.Introduction -- 1.1.Introduction -- 1.2.An Overview of the Book -- 2.Locomotion -- 2.1.Introduction -- 2.1.1.Key issues for locomotion -- 2.2.Legged Mobile Robots -- 2.2.1.Leg configurations and stability -- 2.2.2.Consideration of dynamics -- 2.2.3.Examples of legged robot locomotion -- 2.3.Wheeled Mobile Robots -- 2.3.1.Wheeled locomotion: The design space -- 2.3.2.Wheeled locomotion: Case studies -- 2.4.Aerial Mobile Robots -- 2.4.1.Introduction -- 2.4.2.Aircraft configurations -- 2.4.3.State of the art in autonomous VTOL -- 2.5.Problems -- 3.Mobile Robot Kinematics -- 3.1.Introduction -- 3.2.Kinematic Models and Constraints -- 3.2.1.Representing robot position -- 3.2.2.Forward kinematic models -- 3.2.3.Wheel kinematic constraints -- 3.2.4.Robot kinematic constraints -- 3.2.5.Examples: Robot kinematic models and constraints
    3.3.Mobile Robot Maneuverability -- 3.3.1.Degree of mobility -- 3.3.2.Degree of steerability -- 3.3.3.Robot maneuverability -- 3.4.Mobile Robot Workspace -- 3.4.1.Degrees of freedom -- 3.4.2.Holonomic robots -- 3.4.3.Path and trajectory considerations -- 3.5.Beyond Basic Kinematics -- 3.6.Motion Control (Kinematic Control) -- 3.6.1.Open loop control (trajectory-following) -- 3.6.2.Feedback control -- 3.7.Problems -- 4.Perception -- 4.1.Sensors for Mobile Robots -- 4.1.1.Sensor classification -- 4.1.2.Characterizing sensor performance -- 4.1.3.Representing uncertainty -- 4.1.4.Wheel/motor sensors -- 4.1.5.Heading sensors -- 4.1.6.Accelerometers -- 4.1.7.Inertial measurement unit (IMU) -- 4.1.8.Ground beacons -- 4.1.9.Active ranging -- 4.1.10.Motion/speed sensors -- 4.1.11.Vision sensors -- 4.2.Fundamentals of Computer Vision -- 4.2.1.Introduction -- 4.2.2.The digital camera -- 4.2.3.Image formation -- 4.2.4.Omnidirectional cameras
    4.2.5.Structure from stereo -- 4.2.6.Structure from motion -- 4.2.7.Motion and optical flow -- 4.2.8.Color tracking -- 4.3.Fundamentals of Image Processing -- 4.3.1.Image filtering -- 4.3.2.Edge detection -- 4.3.3.Computing image similarity -- 4.4.Feature Extraction -- 4.5.Image Feature Extraction: Interest Point Detectors -- 4.5.1.Introduction -- 4.5.2.Properties of the ideal feature detector -- 4.5.3.Corner detectors -- 4.5.4.Invariance to photometric and geometric changes -- 4.5.5.Blob detectors -- 4.6.Place Recognition -- 4.6.1.Introduction -- 4.6.2.From bag of features to visual words -- 4.6.3.Efficient location recognition by using an inverted file -- 4.6.4.Geometric verification for robust place recognition -- 4.6.5.Applications -- 4.6.6.Other image representations for place recognition -- 4.7.Feature Extraction Based on Range Data (Laser, Ultrasonic) -- 4.7.1.Line fitting -- 4.7.2.Six line-extraction algorithms
    4.7.3.Range histogram features -- 4.7.4.Extracting other geometric features -- 4.8.Problems -- 5.Mobile Robot Localization -- 5.1.Introduction -- 5.2.The Challenge of Localization: Noise and Aliasing -- 5.2.1.Sensor noise -- 5.2.2.Sensor aliasing -- 5.2.3.Effector noise -- 5.2.4.An error model for odometric position estimation -- 5.3.To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- 5.4.Belief Representation -- 5.4.1.Single-hypothesis belief -- 5.4.2.Multiple-hypothesis belief -- 5.5.Map Representation -- 5.5.1.Continuous representations -- 5.5.2.Decomposition strategies -- 5.5.3.State of the art: Current challenges in map representation -- 5.6.Probabilistic Map-Based Localization -- 5.6.1.Introduction -- 5.6.2.The robot localization problem -- 5.6.3.Basic concepts of probability theory -- 5.6.4.Terminology -- 5.6.5.The ingredients of probabilistic map-based localization
    5.6.6.Classification of localization problems -- 5.6.7.Markov localization -- 5.6.8.Kalman filter localization -- 5.7.Other Examples of Localization Systems -- 5.7.1.Landmark-based navigation -- 5.7.2.Globally unique localization -- 5.7.3.Positioning beacon systems -- 5.7.4.Route-based localization -- 5.8.Autonomous Map Building -- 5.8.1.Introduction -- 5.8.2.SLAM: The simultaneous localization and mapping problem -- 5.8.3.Mathematical definition of SLAM -- 5.8.4.Extended Kalman Filter (EKF) SLAM -- 5.8.5.Visual SLAM with a single camera -- 5.8.6.Discussion on EKF SLAM -- 5.8.7.Graph-based SLAM -- 5.8.8.Particle filter SLAM -- 5.8.9.Open challenges in SLAM -- 5.8.10.Open source SLAM software and other resources -- 5.9.Problems -- 6.Planning and Navigation -- 6.1.Introduction -- 6.2.Competences for Navigation: Planning and Reacting -- 6.3.Path Planning -- 6.3.1.Graph search -- 6.3.2.Potential field path planning
    6.4.Obstacle avoidance -- 6.4.1.Bug algorithm -- 6.4.2.Vector field histogram -- 6.4.3.The bubble band technique -- 6.4.4.Curvature velocity techniques -- 6.4.5.Dynamic window approaches -- 6.4.6.The Schlegel approach to obstacle avoidance -- 6.4.7.Nearness diagram -- 6.4.8.Gradient method -- 6.4.9.Adding dynamic constraints -- 6.4.10.Other approaches -- 6.4.11.Overview -- 6.5.Navigation Architectures -- 6.5.1.Modularity for code reuse and sharing -- 6.5.2.Control localization -- 6.5.3.Techniques for decomposition -- 6.5.4.Case studies: tiered robot architectures -- 6.6.Problems -- Bibliography -- Books -- Papers -- Referenced Webpages.
  • Contributor: Siegwart, Roland [Author]; Nourbakhsh, Illah Reza [Author]; Scaramuzza, Davide [Author]
  • Published: Cambridge, Massachusetts; London, England: MIT Press, [2011]
  • Published in: Intelligent robotics and autonomous agents
  • Issue: 2nd edition
  • Extent: xvi, 453 Seiten; Illustrationen, Diagramme
  • Language: English
  • ISBN: 0262015358; 9780262015356
  • RVK notation: ST 308 : Robotics, CAD, CAM, CAE etc.
    ZQ 6230 : Mobiler Roboter, Robotersysteme
  • Keywords: Autonomer Roboter
    Robotik > Autonomer Roboter > Mobiler Roboter > Kinematik > Bahnplanung > Navigation
    Autonomer Roboter
  • Origination:
  • Footnote: Literaturverzeichnis: Seiten 425-445

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  • Shelf-mark: ZQ 6230 S571(2)
  • Item ID: 34781011