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E-raamat: Sensor Modelling and Data Processing for Autonomous Navigation illustrated edition [World Scientific e-raamat]

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Sensor Modelling and Data Processing for Autonomous Navigation illustrated editionSuurem pilt
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Raamatu kodulehekülg: http://www.worldscientific.com/worldscibooks/10.1142/3814#t=toc
This invaluable book presents an unbiased framework for modelling and using sensors to aid mobile robot navigation. It addresses the problem of accurate and reliable sensing in confined environments and makes a detailed analysis of the design and construction of a low cost optical range finder. This is followed by a quantitative model for determining the sources and propagation of noise within the sensor. The physics behind the causes of erroneous data is also used to derive a model for detecting and labelling such data as false. In addition, the author's data-processing algorithms are applied to the problem of environmental feature extraction. This forms the basis of a solution to the problem of mobile robot localisation. The book develops a relationship between the kinematics of a mobile robot during the execution of successive manoeuvres, and the sensed features. Results which update a mobile vehicle's position using features from 2D and 3D scans are presented.
Preface v
Acknowledgments vii
List of Figures
xiii
Introduction
1(10)
Part I: Sensor Design and Modelling 11(140)
Range Sensing in Confined Environments
13(27)
Introduction
13(1)
Stereo Vision for Range Determination
14(6)
Disparity between Two Images
15(1)
General Case --- Camera Orientation
16(1)
Calculation of Depth
17(1)
Finding Conjugate Points
18(2)
Optical Sensing --- Controlling the Illumination
20(8)
Active Triangulation
20(2)
Synchronised Scanning
22(3)
Structured Light, Line Projection Systems
25(2)
Light Detection and Ranging (LIDAR)
27(1)
Ultrasonic Range Sensing
28(7)
Sonar --- The Physics of Reflection
30(5)
Summary
35(5)
Lidar Sensor Design --- Electronic Requirements
40(40)
Introduction
40(1)
Lidar Range Measurement Methods
41(3)
Time-of-flight Pulse Lidars
41(1)
F.M.C.W. Lidars
42(1)
A.M.C.W. Lidars
43(1)
A.M.C.W Lidar Modules
44(3)
Current to Light Conversion --- The Transmitter Module
47(3)
The Receiver Module --- Opto-electronic Amplification
50(10)
Light to Current Conversion
50(6)
Lambertian Reflection and Signal Reception
56(2)
Low Receiver Band-width Signal Selection
58(2)
Dynamic Range Compression
60(2)
Frequency Reduction
62(2)
Relative Phase Discrimination
64(9)
Phase Detection
71(2)
Output Filter Stage
73(2)
Received Signal Amplitude Detection
75(2)
Summary
77(3)
Lidar Sensor Design --- Mechanical and Optical Requirements
80(25)
Introduction
80(1)
The Scanning Mechanism --- Sensor Reachability
81(8)
Twin Motor Direct Drive Mechanism
84(2)
Single Motor with Synchronising Mechanics
86(1)
Two Fixed Motors with Differential Mechanics
87(2)
Practical Implementation of the Scanning Mechanism
89(3)
Practical Realisation of Solution 3
89(2)
Practical Realisation of Solution 1
91(1)
Coaxial Optical Alignment
92(2)
Focusing the Received Light Signal
94(3)
Physical Compactness and Overall Dimensions
97(4)
Optical Transmitter Construction
101(2)
Summary
103(2)
Quantitative Sensor Modelling --- Noise Analysis
105(21)
Introduction
105(2)
Sensor Noise Revisited
107(5)
Noise Propagation Within the Sensor
108(3)
The Nature of the Range Distribution
111(1)
Averaging Several Range Estimates
112(1)
Calibrating the Sensor --- Results
113(5)
Amplitude Induced Phase Shifts
116(1)
Range Variance
117(1)
2D Range/Amplitude Scanning --- Results
118(3)
Temporal Range Data Averaging
120(1)
3D Range/Amplitude Scanning --- Results
121(2)
Summary
123(3)
Qualitative Sensor Modelling --- False Data
126(25)
Introduction
126(2)
Discontinuous Points --- Mixed Pixels
128(4)
Simultaneous Reflection of Signals from Two Targets
132(4)
Discontinuity Detection
136(7)
False Detection and Range Sensitivity
139(2)
Surface Reflectance Sensitivity
141(2)
Practical Implementation and Results
143(4)
Summary
147(4)
Part II: Mobile Robot Navigation Oriented Signal Processing 151(55)
Environmental Feature Extraction
153(15)
Introduction
153(2)
Feature Extraction --- Edge Detection
155(5)
Edge Detection --- The Extended Kalman Filter
155(3)
Validation Gate Formation
158(1)
Filter Initialisation
159(1)
Edge Detection: Results
160(6)
Summary
166(2)
Sensor Driven Mobile Robot Localisation
168(22)
Introduction
168(3)
Mobile Robot Localisation
171(2)
Mobile Robot Kinematics --- The System Model
171(1)
2D Scanning: The Observation Model
172(1)
3D Scanning: The Observation Model
173(1)
The Localisation Cycle
173(3)
Filter Initialisation
176(1)
Localisation Results
177(5)
Localisation with an a priori Map
177(1)
Localisation without an a priori Map
178(4)
3D Mobile Robot Localisation
182(5)
Summary
187(3)
Application: Mobile Robot Path Planning
190(10)
Introduction
190(3)
Edge --- Target Relationship
193(1)
Target Validity
194(1)
Target Reachability
195(3)
Dynamic Target Production
198(1)
Summary
199(1)
Conclusions and Future Research Directives
200(6)
Conclusions
200(1)
Future Research Directives
201(5)
A Discontinuity Detection --- Second Differential S as a Function of Time 206(3)
Bibliography 209(13)
Index 222


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