Amazon cover image
Image from Amazon.com
Image from Google Jackets
Image from OpenLibrary

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

By: Contributor(s): Material type: TextTextSeries: Intelligent robotics and autonomous agentsPublication details: Cambridge, Mass. : MIT Press, c2011.Edition: 2nd edDescription: xvi, 453 p. : ill. ; 24 cmISBN:
  • 9780262015356 (hardcover : alk. paper)
  • 0262015358 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 629.8932SIE/A 22
Other classification:
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 -- 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Optoelectronics Dept. of Optoelectronics 629.8932SIE/A (Browse shelf(Opens below)) Available DOP3572

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 -- 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.

There are no comments on this title.

to post a comment.