Vision and navigation : the Carnegie Mellon Navlab

edited by Charles E. Thorpe ; with a foreword by Takeo Kanade

[目次]

  • 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.

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この本の情報

書名 Vision and navigation : the Carnegie Mellon Navlab
著作者等 Thorpe, Charles E.
Thorpe Charles F.
シリーズ名 The Kluwer international series in engineering and computer science
出版元 Kluwer Academic Publishers
刊行年月 c1990
ページ数 xiv, 370 p.
大きさ 25 cm
ISBN 0792390687
NCID BA10216983
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言語 英語
出版国 アメリカ合衆国
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