High-Speed Range Estimation Based on Intensity Gradient Analysis
A fast and reasonably accurate perception of the environment is essential for successful navigation of an autonomous agent. Although many modes of sensing are applicable to this task and have been used, vision remains the most appealing due to its passive nature, good range, and resolution. Most vis...
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| Format: | eBook |
| Language: | English |
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New York, NY
Springer New York
1991, 1991
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| Edition: | 1st ed. 1991 |
| Series: | Springer Series in Perception Engineering
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1 Introduction
- 1.1 Purpose
- 1.2 Philosophy
- 1.3 The Structure of This Thesis
- 2 Approaches to the Depth Recovery Problem
- 2.1 Sensing Modalities
- 2.2 Vision as a Primary Mode of Sensing
- 2.3 Literature Survey
- 2.4 Summary
- 3 Depth Recovery
- 3.1 Depth Recovery Using Translational Sensor Motion
- 3.2 Special Case: Axial Camera Motion
- 3.3 Special Case: Lateral Camera Motion
- 3.4 The Parameters Needed for Depth Recovery
- 4 Theoretical Basis for IGA
- 4.1 Acquiring a Sequence of Images
- 4.2 Two Ideas and Their Implications
- 5 Intensity Gradient Analysis
- 5.1 Isolating Fixed Image Displacements Using Intensity Gradients
- 5.2 Why Do More Work?
- 5.3 Extending to Two Dimensions
- 5.4 The IGA Algorithm
- 6 Implementation Issues
- 6.1 Problems with Real-World Sensors
- 6.2 Uncertainty in the Camera Motion Parameters
- 6.3 Moving Objects
- 6.4 Summary
- 7 Fixed Disparity Surfaces
- 7.1 Examples of FDS’s
- 7.2 Interpreting FDS’s
- 7.3 Fixed Disparity Surfaces and Conventional Stereo
- 8 Experiments
- 8.1 Experimental Setup
- 8.2 Calibration Procedures
- 8.4 Outdoor Scenes
- 8.5 Conclusions
- 9 An Application: Vision-Guided Navigation Using IGA
- 9.1 Navigation
- 9.2 Experimental Setup
- 9.3 Experiments
- 10 Conclusion
- 10.1 Future Research
- 10.2 Contribution