Introduction to autonomous mobile robots

 Content: 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.
 Abstract:             
              The second edition of a comprehensive introduction to all aspects of mobile robotics, from algorithms to mechanisms.                 Read more...