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...
