Asia Pacific University Library catalogue


Introduction to autonomous mobile robots / Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza.

By: Siegwart, RolandContributor(s): Nourbakhsh, Illah Reza, 1970- | Scaramuzza, DavideMaterial type: TextTextSeries: Intelligent robotics and autonomous agentsPublication details: Cambridge, Mass. : MIT Press, c2011Edition: 2nd edDescription: xvi, 453 p. : ill. ; 24 cmISBN: 9780262015356 (hbk.)Subject(s): Mobile robots | Autonomous robotsDDC classification: 629.892 LOC classification: TJ211.415 | .S54 2011
Contents:
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 -- g
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 -- 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.
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Item type Current library Collection Call number Copy number Status Date due Barcode
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Open Shelf 		Book TJ211.415 .S54 2011 c.1 (Browse shelf (Opens below)) 1 Available (No use restrictions) 00024864

Includes bibliographical references and index.

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 -- g

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 -- 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.

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