Siegwart, Roland.
Introduction to autonomous mobile robots / Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza. - 2nd ed. - Cambridge, Mass. : MIT Press, c2011. - xvi, 453 p. : ill. ; 24 cm. - Intelligent robotics and autonomous agents .
Includes bibliographical references and index.
Introduction -- Introduction -- An Overview of the Book -- Locomotion -- Introduction -- Key issues for locomotion -- Legged Mobile Robots -- Leg configurations and stability -- Consideration of dynamics -- Examples of legged robot locomotion -- Wheeled Mobile Robots -- Wheeled locomotion: The design space -- Wheeled locomotion: Case studies -- Aerial Mobile Robots -- Introduction -- Aircraft configurations -- State of the art in autonomous VTOL -- Problems -- Mobile Robot Kinematics -- Introduction -- Kinematic Models and Constraints -- Representing robot position -- Forward kinematic models -- Wheel kinematic constraints -- Robot kinematic constraints -- Examples: Robot kinematic models and constraints Machine generated contents note: 1. 1.1. 1.2. 2. 2.1. 2.1.1. 2.2. 2.2.1. 2.2.2. 2.2.3. 2.3. 2.3.1. 2.3.2. 2.4. 2.4.1. 2.4.2. 2.4.3. 2.5. 3. 3.1. 3.2. 3.2.1. 3.2.2. 3.2.3. 3.2.4. 3.2.5. Mobile Robot Maneuverability -- Degree of mobility -- 3.3. 3.3.1. g Structure from stereo -- Structure from motion -- Motion and optical flow -- Color tracking -- Fundamentals of Image Processing -- Image filtering -- Edge detection -- Computing image similarity -- Feature Extraction -- Image Feature Extraction: Interest Point Detectors -- Introduction -- Properties of the ideal feature detector -- Corner detectors -- Invariance to photometric and geometric changes -- Blob detectors -- Place Recognition -- Introduction -- From bag of features to visual words -- Efficient location recognition by using an inverted file -- Geometric verification for robust place recognition -- Applications -- Other image representations for place recognition -- Feature Extraction Based on Range Data (Laser, Ultrasonic) -- Line fitting -- Six line-extraction algorithms 4.2.5. 4.2.6. 4.2.7. 4.2.8. 4.3. 4.3.1. 4.3.2. 4.3.3. 4.4. 4.5. 4.5.1. 4.5.2. 4.5.3. 4.5.4. 4.5.5. 4.6. 4.6.1. 4.6.2. 4.6.3. 4.6.4. 4.6.5. 4.6.6. 4.7. 4.7.1. 4.7.2. Range histogram features -- Extracting other geometric features -- Problems -- Mobile Robot Localization -- Introduction -- The Challenge of Localization: Noise and Aliasing -- Sensor noise -- Sensor aliasing -- Effector noise -- An error model for odometric position estimation -- To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- Belief Representation -- Single-hypothesis belief -- Multiple-hypothesis belief -- Map Representation -- Continuous representations -- Decomposition strategies -- State of the art: Current challenges in map representation -- Probabilistic Map-Based Localization -- Introduction -- The robot localization problem -- Basic concepts of probability theory -- Terminology -- The ingredients of probabilistic map-based localization 4.7.3. 4.7.4. 4.8. 5. 5.1. 5.2. 5.2.1. 5.2.2. 5.2.3. 5.2.4. 5.3. 5.4. 5.4.1. 5.4.2. 5.5. 5.5.1. 5.5.2. 5.5.3. 5.6. 5.6.1. 5.6.2. 5.6.3. 5.6.4. 5.6.5. Classification of localization problems -- Markov localization -- Kalman filter localization -- Other Examples of Localization Systems -- Landmark-based navigation -- Globally unique localization -- Positioning beacon systems -- Route-based localization -- Autonomous Map Building -- Introduction -- SLAM: The simultaneous localization and mapping problem -- Mathematical definition of SLAM -- Extended Kalman Filter (EKF) SLAM -- Visual SLAM with a single camera -- Discussion on EKF SLAM -- Graph-based SLAM -- Particle filter SLAM -- Open challenges in SLAM -- Open source SLAM software and other resources -- Problems -- Planning and Navigation -- Introduction -- Competences for Navigation: Planning and Reacting -- Path Planning -- Graph search -- Potential field path planning 5.6.6. 5.6.7. 5.6.8. 5.7. 5.7.1. 5.7.2. 5.7.3. 5.7.4. 5.8. 5.8.1. 5.8.2. 5.8.3. 5.8.4. 5.8.5. 5.8.6. 5.8.7. 5.8.8. 5.8.9. 5.8.10. 5.9. 6. 6.1. 6.2. 6.3. 6.3.1. 6.3.2. Obstacle avoidance -- Bug algorithm -- Vector field histogram -- The bubble band technique -- Curvature velocity techniques -- Dynamic window approaches -- The Schlegel approach to obstacle avoidance -- Nearness diagram -- Gradient method -- Adding dynamic constraints -- Other approaches -- Overview -- Navigation Architectures -- Modularity for code reuse and sharing -- Control localization -- Techniques for decomposition -- Case studies: tiered robot architectures -- Problems -- Bibliography -- Books -- Papers -- Referenced Webpages. 6.4. 6.4.1. 6.4.2. 6.4.3. 6.4.5. 6.4.6. 6.4.7. 6.4.8. 6.4.9. 6.4.10. 6.4.11. 6.5. 6.5.1. 6.5.2. 6.5.3. 6.5.4. 6.6.
9780262015356 (hbk.)
2010028053
015690249 Uk
Mobile robots.
Autonomous robots.
TJ211.415 / .S54 2011
629.892 / SIE 2011
Introduction to autonomous mobile robots / Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza. - 2nd ed. - Cambridge, Mass. : MIT Press, c2011. - xvi, 453 p. : ill. ; 24 cm. - Intelligent robotics and autonomous agents .
Includes bibliographical references and index.
Introduction -- Introduction -- An Overview of the Book -- Locomotion -- Introduction -- Key issues for locomotion -- Legged Mobile Robots -- Leg configurations and stability -- Consideration of dynamics -- Examples of legged robot locomotion -- Wheeled Mobile Robots -- Wheeled locomotion: The design space -- Wheeled locomotion: Case studies -- Aerial Mobile Robots -- Introduction -- Aircraft configurations -- State of the art in autonomous VTOL -- Problems -- Mobile Robot Kinematics -- Introduction -- Kinematic Models and Constraints -- Representing robot position -- Forward kinematic models -- Wheel kinematic constraints -- Robot kinematic constraints -- Examples: Robot kinematic models and constraints Machine generated contents note: 1. 1.1. 1.2. 2. 2.1. 2.1.1. 2.2. 2.2.1. 2.2.2. 2.2.3. 2.3. 2.3.1. 2.3.2. 2.4. 2.4.1. 2.4.2. 2.4.3. 2.5. 3. 3.1. 3.2. 3.2.1. 3.2.2. 3.2.3. 3.2.4. 3.2.5. Mobile Robot Maneuverability -- Degree of mobility -- 3.3. 3.3.1. g Structure from stereo -- Structure from motion -- Motion and optical flow -- Color tracking -- Fundamentals of Image Processing -- Image filtering -- Edge detection -- Computing image similarity -- Feature Extraction -- Image Feature Extraction: Interest Point Detectors -- Introduction -- Properties of the ideal feature detector -- Corner detectors -- Invariance to photometric and geometric changes -- Blob detectors -- Place Recognition -- Introduction -- From bag of features to visual words -- Efficient location recognition by using an inverted file -- Geometric verification for robust place recognition -- Applications -- Other image representations for place recognition -- Feature Extraction Based on Range Data (Laser, Ultrasonic) -- Line fitting -- Six line-extraction algorithms 4.2.5. 4.2.6. 4.2.7. 4.2.8. 4.3. 4.3.1. 4.3.2. 4.3.3. 4.4. 4.5. 4.5.1. 4.5.2. 4.5.3. 4.5.4. 4.5.5. 4.6. 4.6.1. 4.6.2. 4.6.3. 4.6.4. 4.6.5. 4.6.6. 4.7. 4.7.1. 4.7.2. Range histogram features -- Extracting other geometric features -- Problems -- Mobile Robot Localization -- Introduction -- The Challenge of Localization: Noise and Aliasing -- Sensor noise -- Sensor aliasing -- Effector noise -- An error model for odometric position estimation -- To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- Belief Representation -- Single-hypothesis belief -- Multiple-hypothesis belief -- Map Representation -- Continuous representations -- Decomposition strategies -- State of the art: Current challenges in map representation -- Probabilistic Map-Based Localization -- Introduction -- The robot localization problem -- Basic concepts of probability theory -- Terminology -- The ingredients of probabilistic map-based localization 4.7.3. 4.7.4. 4.8. 5. 5.1. 5.2. 5.2.1. 5.2.2. 5.2.3. 5.2.4. 5.3. 5.4. 5.4.1. 5.4.2. 5.5. 5.5.1. 5.5.2. 5.5.3. 5.6. 5.6.1. 5.6.2. 5.6.3. 5.6.4. 5.6.5. Classification of localization problems -- Markov localization -- Kalman filter localization -- Other Examples of Localization Systems -- Landmark-based navigation -- Globally unique localization -- Positioning beacon systems -- Route-based localization -- Autonomous Map Building -- Introduction -- SLAM: The simultaneous localization and mapping problem -- Mathematical definition of SLAM -- Extended Kalman Filter (EKF) SLAM -- Visual SLAM with a single camera -- Discussion on EKF SLAM -- Graph-based SLAM -- Particle filter SLAM -- Open challenges in SLAM -- Open source SLAM software and other resources -- Problems -- Planning and Navigation -- Introduction -- Competences for Navigation: Planning and Reacting -- Path Planning -- Graph search -- Potential field path planning 5.6.6. 5.6.7. 5.6.8. 5.7. 5.7.1. 5.7.2. 5.7.3. 5.7.4. 5.8. 5.8.1. 5.8.2. 5.8.3. 5.8.4. 5.8.5. 5.8.6. 5.8.7. 5.8.8. 5.8.9. 5.8.10. 5.9. 6. 6.1. 6.2. 6.3. 6.3.1. 6.3.2. Obstacle avoidance -- Bug algorithm -- Vector field histogram -- The bubble band technique -- Curvature velocity techniques -- Dynamic window approaches -- The Schlegel approach to obstacle avoidance -- Nearness diagram -- Gradient method -- Adding dynamic constraints -- Other approaches -- Overview -- Navigation Architectures -- Modularity for code reuse and sharing -- Control localization -- Techniques for decomposition -- Case studies: tiered robot architectures -- Problems -- Bibliography -- Books -- Papers -- Referenced Webpages. 6.4. 6.4.1. 6.4.2. 6.4.3. 6.4.5. 6.4.6. 6.4.7. 6.4.8. 6.4.9. 6.4.10. 6.4.11. 6.5. 6.5.1. 6.5.2. 6.5.3. 6.5.4. 6.6.
9780262015356 (hbk.)
2010028053
015690249 Uk
Mobile robots.
Autonomous robots.
TJ211.415 / .S54 2011
629.892 / SIE 2011