Vision and Navigation

1. Introduction.- 1.1. Mobile Robots.- 1.2. Overview.- 1.3. Acknowledgments.- 2. Color Vision for Road Following.- 2.1. Introduction.- 2.2. SCARF.- 2.2.1. Two Camera System.- 2.2.2 Classifier.- 2.2.3. Interpretation.- 2.2.4. Model Formation.- 2.2.5. WARP Implementation.- 2.2.6. Discussion.- 2.3. UNSCARF.- 2.3.1. Unsupervised Classification Segmentation.- 2.3.2. Interpretation.- 2.3.3. Discussion and Future Work.- 2.4. Results and Conclusions.- 2.5. References.- 3. Explicit Models for Robot Road Following.- 3.1 Implicit Models Considered Harmful.- 3.2 Systems, Models, and Assumptions.- 3.2.1 SCARF: Color Classification.- 3.2.2 Maryland.- 3.2.3 VITS.- 3.2.4 Dickmanns and Grafe.- 3.3 FERMI.- 3.3.1 Explicit Models.- 3.3.2 Trackers.- 3.3.3 Tracker Fusion.- 3.3.4 Interpretations.- 3.3.5 Current Status.- 3.4 References.- 4. An Approach to Knowledge-Based Interpretation of Outdoor Natural Color Road Scenes.- 4.1. Abstract.- 4.2. Introduction.- 4.3. Related Work.- 4.4. Adjustable Explicit Scene Models and the Interpretation Cycle.- 4.4.1. Adjustable Explicit Scene Models.- 4.4.2. Interpretation Cycle.- 4.5. System Overview.- 4.6. Results of the Road Scene Interpretation.- 4.7. The Road Scene Interpretation System in Detail.- 4.7.1. Feature Extraction and Intermediate Representation.- 4.7.2. Initial Hypothesis Generation.- 4.7.3. Context Control.- 4.7.4. Evaluation.- 4.7.5. Modeling.- 4.7.6. Extrapolation.- 4.7.7. Road Map Generation.- 4.7.8. System Analysis.- 4.8. Future Work.- 4.8.1. Inexhaustive Region Segmentation for a High-level Interpreter.- 4.8.2. Adaptive Data Abstraction.- 4.9. Conclusion.- 4.10. Acknowledgement.- 4.11. References.- 5. Neural Network Based Autonomous Navigation.- 5.1. Introduction.- 5.2. Network Architecture.- 5.3. Training And Performance.- 5.4. Network Representation.- 5.5. Discussion And Extensions.- 5.6. Conclusion.- 5.7. References.- 6. Car Recognition for the CMU Navlab.- 6.1 Introduction.- 6.1.1 The function of object recognition in autonomous navigation.- 6.1.2 Choice of domain.- 6.1.3 The goals and state of the research.- 6.2 Related work.- 6.3 The LASSIE object recognition program.- 6.3.1 Overview.- 6.3.2 Description of the segmentation and grouping stages.- 6.3.3 Feature-fetchers and the search for initial matches.- 6.3.4 Verification of initial matches.- 6.4 Results.- 6.5 Directions for future work.- 6.6 Summary.- 6.7 References.- 7. Building and Navigating Maps of Road Scenes Using Active Range and Reflectance Data.- 7.1. Introduction.- 7.2. Following roads using active reflectance images.- 7.3. Building maps from range and reflectance images.- 7.4. Map-based road following.- 7.5. Conclusion.- 7.6. References.- 8. 3-D Vision Techniques for Autonomous Vehicles.- 8.1. Introduction.- 8.2. Active range and reflectance sensing.- 8.2.1. From range pixels to points in space.- 8.2.2. Reflectance images.- 8.2.3. Resolution and noise.- 8.3. Terrain representations.- 8.3.1. The elevation map as the data structure for terrain representation.- 8.3.2. Terrain representations and path planners.- 8.3.3. Low resolution: Obstacle map.- 8.3.4. Medium resolution: Polygonal terrain map.- 8.3.5. High resolution: Elevation maps for rough terrain.- 8.4. Combining multiple terrain maps.- 8.4.1. The terrain matching problem: iconic vs. feature-based.- 8.4.2. Feature-based matching.- 8.4.3. Iconic matching from elevation maps.- 8.5. Combining range and intensity data.- 8.5.1. The geometry of video cameras.- 8.5.2. The registration problem.- 8.5.3. Application to outdoor scene analysis.- 8.6. Conclusion.- 8.7. References.- 9. The CODGER System for Mobile Robot Navigation.- 9.1 Introduction.- 9.2 Overview of the CODGER System.- 9.3 Data Storage and Transfer.- 9.3.1 Database Tokens.- 9.3.2 Synchronization Primitives.- 9.4 Geometric Representation and Reasoning.- 9.4.1 Geometric Data and Indexing.- 9.4.2 Frames and Frame Generators.- 9.4.3 Geometric Consistency and AffIxment Groups.- 9.5 Conclusions.- 9.6 References.- 10. The Driving Pipeline: A Driving Control Scheme for Mobile Robots.- 10.1 Introduction.- 10.2 Processing Steps and Driving Unit.- 10.2.1 Prediction and the Driving Unit.- 10.2.2 Perception and Driving Unit.- 10.2.3 Environment Modeling and the Driving Unit.- 10.2.4 Local Path Planning and the Driving Unit.- 10.2.5 Vehicle Control and the Driving Unit.- 10.3 Continuous Motion, Adaptive Control, and the Driving Pipeline.- 10.3.1 Pipelined Execution for Continuous Motion.- 10.3.2 Execution Intervals of the Driving Pipeline.- 10.3.3 Parallelism in the Driving Pipeline.- 10.3.4 Vehicle Speed and Driving Pipeline.- 10.4 The Driving Pipeline in Action: Experimental Results.- 10.4.1 Implementing the Driving Pipeline.- 10.4.2 Processing Steps and Driving Units.- 10.4.3 Pipeline Execution and Parallelism.- 10.4.4 Execution Intervals.- 10.4.5 Vehicle Speed.- 10.4.6 Sensor Aiming.- 10.5 Conclusion.- 10.6 References.- 11. Multi-Resolution Constraint Modeling for Mobile Robot Planning.- 11.1 Introduction.- 11.2 The Local Navigation Problem.- 11.2.1 Goal satisfaction.- 11.2.2 Environmental admissibility.- 11.2.3 Kinematic constraints.- 11.2.4 Uncertainty in path execution.- 11.3 Finding Trajectories.- 11.3.1 Searching the constraint space.- 11.3.2 Testing subspaces for constraint satisfaction.- 11.4 Experiments.- 11.5 Conclusions.- 11.6 Acknowledgements.- 11.7 References.- 12. Navlab: An Autonomous Navigation Testbed.- 12.1 Introduction.- 12.2 Controller.- 12.2.1 System Architecture.- 12.2.2 Virtual Vehicle.- 12.2.3 Motion Control.- 12.3 Vehicle Shell.- 12.3.1 Exterior Design.- 12.3.2 Interior Design.- 12.4 Locomotion.- 12.4.1 Steering.- 12.4.2 Drive.- 12.5 Electrical System.- 12.5.1 AC Power.- 12.6 Telemetry.- 12.7 Perceptive Sensing and Computing.- 12.7.1 Video.- 12.7.2 Laser Ranging.- 12.7.3 Computing Configuration for Sensing.- 12.8 Conclusion.- 13. Vehicle and Path Models for Autonomous Navigation.- 13.1 Introduction.- 13.2 Vehicle Representation.- 13.2.1 Vehicle Kinematics.- 13.2.2 Vehicle Dynamics.- 13.2.3 Systemic Effects.- 13.3 Path Representation.- 13.4 Path Tracking.- 13.4.1 Feedback Control.- 13.4.2 Feedforward Control.- 13.4.3 Speed Control.- 13.5 Results.- 13.6 Conclusions.- 13.7 References.- 14. The Warp Machine on Navlab.- 14.1 Introduction.- 14.2 History of the Warp Machine on Navlab.- 14.3 FIDO.- 14.3.1 FIDO Algorithm.- 14.3.2 Implementation of FIDO on Warp.- 14.3.3 Performance of the Vision Modules.- 14.4 SCARF.- 14.4.1 SCARF Algorithm.- 14.4.2 Implementation of SCARF on the Warp Machine.- 14.4.3 Performance of SCARF Implementations.- 14.5 ALVINN.- 14.6 Evaluation of the Warp Machine on Navlab.- 14.6.1 Warp Hardware.- 14.6.2 Warp Software.- 14.7 Conclusions.- 14.8 References.- 15. Outdoor Visual Navigation for Autonomous Robots.- 15.1 Introduction.- 15.2 Example Systems.- 15.2.1 Navlab Controller and Architecture.- 15.2.2 Autonomous Mail Vehicle.- 15.2.3 Generic Cross-Country Vehicle.- 15.2.4 Planetary Exploration by Walking Robot.- 15.3 Discussion and Conclusions.- 15.4 Acknowledgements.- 15.5 References.