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E-raamat: Motion Vision: Design of compact motion sensing solutions for navigation of autonomous systems

(Griffith University, Institute for Integrated and Intelligent Systems, Brisbane, Australia), (Griffith University, Institute for Integrated and Intelligent Systems, Brisbane, Australia)
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  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 23-Sep-2011
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9780863411588
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  • Formaat: PDF+DRM
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 23-Sep-2011
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9780863411588
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Kolodko and Vlacic, both affiliated with the Intelligent Control Systems Laboratory, Griffith University, Australia, focus on the design, development, and implementation of motion measuring sensors, in this guide for sensor designers and for advanced students with background in programming and basic digital logic. They begin by looking at the problem of motion estimation from biological, algorithmic, and digital perspectives, then describe an algorithm that fits with the motion processing model and hardware and software constraints, and detail implementation of the algorithm in digital hardware. The book is distributed in the US by Books International. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

This comprehensive new book deals with motion estimation for autonomous systems from a biological, algorithmic and digital perspective. An algorithm, which is based on the optical flow constraint equation, is described in detail. This algorithm fits with the motion processing model, hardware and software constraints and resolves depth-velocity ambiguity, which is critical for autonomous navigation. There is also extensive coverage on the use of this algorithm in digital hardware and describes both the initial motion processing model, the chosen hardware platforms, and the global function of the system.
Preface xiii
List of abbreviations
xix
Symbols xxi
Typographical conventions xxv
Acknowledgements xxvii
Introduction
1(8)
The intelligent motion measuring sensor
2(7)
Inputs and outputs
2(1)
Real-time motion estimation
3(2)
The motion estimation algorithm
5(2)
The prototype sensor
7(2)
PART 1 -- BACKGROUND
9(92)
Mathematical preliminaries
11(52)
Basic concepts in probability
11(12)
Experiments and trials
12(1)
Sample space and outcome
12(1)
Event
12(1)
Computation of probability
13(1)
Conditional probability
13(1)
Total probability
14(1)
Complement
14(1)
OR
14(1)
And
15(1)
Independent events
16(1)
Bayes theorem
16(1)
Order statistics
16(1)
Random variable
16(1)
Probability Density Function (PDF)
17(1)
Cumulative Distribution Function (CDF)
17(1)
Joint distribution functions
18(1)
Marginal distribution function
19(1)
Independent, identically distributed (iid)
19(1)
Gaussian distribution and the central limit theorem
19(1)
Random or stochastic processes
19(1)
Stationary processes
20(1)
Average
21(1)
Variance
21(1)
Expectation
22(1)
Likelihood
22(1)
Simple estimation problems
23(20)
Linear regression
23(3)
Solving linear regression problems
26(1)
The Hough transform
27(1)
Solving Hough transform problems
28(1)
Multiple linear regression and regularisation
29(4)
Solving the membrane model
33(6)
Location estimates
39(1)
Solving location estimation problems
40(2)
Properties of simple estimators
42(1)
Robust estimation
43(20)
Outliers and leverage points
43(4)
Properties of robust estimators
47(3)
Some robust estimators
50(13)
Motion estimation
63(38)
The motion estimation problem
64(1)
Visual motion estimation
65(30)
Brightness constancy
66(3)
Background subtraction and surveillance
69(1)
Gradient based motion estimation
69(7)
Displaced frame difference
76(3)
Variations of the OFCE
79(2)
Token based motion estimation
81(4)
Frequency domain motion estimation
85(2)
Multiple motions
87(8)
Temporal integration
95(1)
Alternate motion estimation techniques
96(2)
Motion estimation hardware
98(2)
Proposed motion sensor
100(1)
PART 2 -- ALGORITHM DEVELOPMENT
101(70)
Real-time motion processing
103(14)
Frequency domain analysis of image motion
103(3)
Rigid body motion and the pinhole camera model
106(3)
Linking temporal aliasing to the safety margin
109(2)
Scale space
111(2)
Dynamic scale space
113(1)
Issues surrounding a dynamic scale space
114(3)
Motion estimation for autonomous navigation
117(54)
Assumptions, requirements and principles
117(7)
Application
118(1)
Data sources
118(3)
Motion
121(2)
Environment
123(1)
The motion estimation algorithm
124(40)
Inputs and outputs
124(1)
Constraint equation
125(1)
Derivative estimation -- practicalities
126(5)
Effect of illumination change
131(1)
Robust average
132(5)
Comparing our robust average to other techniques
137(9)
Monte Carlo study of the LTSV estimator
146(7)
Computational complexity
153(1)
Dynamic scale space implementation
153(1)
Temporal integration implementation
154(2)
The motion estimation algorithm
156(1)
Simulation results
156(8)
Navigation using the motion estimate
164(7)
PART 3 -- HARDWARE
171(112)
Digital design
173(58)
What is an FPGA?
174(1)
How do I specify what my FPGA does?
174(1)
The FPGA design process in a nutshell
175(2)
Time
177(1)
Our design approach
178(1)
Introducing VHDL
178(23)
VHDL entities and architectures
179(3)
VHDL types and libraries
182(8)
Concurrent and sequential statements
190(9)
Inference
199(2)
Timing constraints
201(3)
General design tips
204(7)
Synchronisation and metastability
204(2)
Limit nesting of if statements
206(1)
Tristate buffers for large multiplexers
206(1)
Tristate buffers
206(1)
Don't gate clocks
207(1)
Register outputs for all blocks
208(1)
Counters
208(1)
Special features
208(1)
Sequential pipelining
208(1)
Use of hierarchy
208(2)
Parentheses
210(1)
Bit width
210(1)
Initialisation
211(1)
Propagation delay
211(1)
Graphical design entry
211(19)
State machines
212(9)
A more complex design
221(9)
Applying our design method
230(1)
Sensor implementation
231(52)
Components
232(5)
Image sensor
232(2)
Range sensor
234(2)
Processing platform
236(1)
PC
236(1)
FPGA system design
237(33)
Boot process
238(1)
Order of operations
239(1)
Memory management
240(4)
RAMIC
244(6)
Buffers
250(3)
Data paths
253(17)
Experimental results
270(7)
Experimental setup
270(1)
Aligning the camera and range sensors
270(3)
Stationary camera
273(1)
Moving camera -- effect of barrel distortion
273(3)
Moving camera -- elimination of barrel distortion
276(1)
Moving camera -- image noise
276(1)
Moving camera -- noise motion and high velocities
277(1)
Implementation statistics
277(1)
Where to from here?
278(5)
Dynamic scale space
278(2)
Extending the LTSV estimator
280(1)
Temporal integration
280(1)
Trimming versus Winsorising
281(1)
Rough ground
281(1)
Extending the hardware
281(2)
PART 4 -- APPENDICES
283(112)
A System timing
285(12)
A.1 Timing for a 512 x 32 pixel image
286(1)
A.2 Control flow for a 512 x 32 pixel image
287(1)
A.3 Timing for a 32 x 32 pixel image
288(1)
A.4 Control flow for a 32 x 32 pixel image
289(1)
A.5 Legend for timing diagrams
290(4)
A.5.1 Note 1: image data clobbering
294(1)
A.5.2 Note 2: the use of n in the timing diagram
295(1)
A.5.3 Note 3: scale space change over process
295(1)
A.5.4 Note 4: the first frame
295(2)
B SDRAM timing
297(4)
B.1 Powerup sequence
297(1)
B.2 Read cycle
298(1)
B.3 Write cycle
298(3)
C FPGA design
301(76)
C.1 Summary of design components
301(5)
C.2 Top level schematic
306(1)
C.3 RAMIC
307(11)
C.4 Buffers
318(15)
C.4.1 Camera data path
333(7)
C.5 Laser and PC data paths
340(4)
C.6 Processing
344(16)
C.7 Miscellaneous components
360(8)
C.8 User constraints file
368(9)
D Simulation of range data
377(18)
Bibliography 395(22)
Index 417


J. Kolodko currently works at the Intelligent Control Systems Laboratory, the Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.



L. Vlacic currently works at the Intelligent Control Systems Laboratory, the Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.