Robotics Vision and Control Fundamental Algorithms In MATLAB 2nd Edition by Peter Corke 3319544128 9783319544120

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ISBN 10: 3319544128

ISBN 13: 9783319544120

Author: Peter Corke

Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. The research community has developed a large body of such algorithms but for a newcomer to the field this can be quite daunting. For over 20 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitionerof both robotics and computer vision. It is written in an accessible but informative style, easy to read and absorb, and includes over 1000 MATLAB and Simulink® examples and over 400 figures. The book is a real walk through the fundamentals of mobile robots, arm robots. then camera models, image processing, feature extraction and multi-view geometry and finally bringing it all together with an extensive discussion of visual servo systems. This second edition is completely revised, updated and extended with coverage of Lie groups, matrix exponentials and twists; inertial navigation; differential drive robots; lattice planners; pose-graph SLAM and map making; restructured material on arm-robot kinematics and dynamics; series-elastic actuators and operational-space control; Lab color spaces; light field cameras; structured light, bundle adjustment and visual odometry; and photometric visual servoing.   “An authoritative book, reaching across fields, thoughtfully conceived andbrilliantly accomplished!” OUSSAMA KHATIB, Stanford

Robotics Vision and Control Fundamental Algorithms In MATLAB 2nd Table of contents:

1 Introduction

1.1 Robots, Jobs and Ethics

1.2 About the Book

1.2.1 MATLAB Software and the Toolboxes

1.2.2 Notation, Conventions and Organization

1.2.3 Audience and Prerequisites

1.2.4 Learning with the Book

1.2.5 Teaching with the Book

1.2.6 Outline

Further Reading

Part I Foundations

2 Representing Position and Orientation

2.1 Working in Two Dimensions (2D)

2.1.1 Orientation in 2-Dimensions

2.1.1.1 Orthonormal Rotation Matrix

2.1.1.2 Matrix Exponential

2.1.2 Pose in 2-Dimensions

2.1.2.1 Homogeneous Transformation Matrix

2.1.2.2 Centers of Rotation

2.1.2.3 Twists in 2D

2.2 Working in Three Dimensions (3D)

2.2.1 Orientation in 3-Dimensions

2.2.1.1 Orthonormal Rotation Matrix

2.2.1.2 Three- Angle Representations

2.2.1.3 Singularities and Gimbal Lock

2.2.1.4 Two Vector Representation

2.2.1.5 Rotation about an Arbitrary Vector

2.2.1.6 Matrix Exponentials

2.2.1.7 Unit Quaternions

2.2.2 Pose in 3-Dimensions

2.2.2.1 Homogeneous Transformation Matrix

2.2.2.2 Vector-Quaternion Pair

2.2.2.3 Twists

2.3 Advanced Topics

2.3.1 Normalization

2.3.2 Understanding the Exponential Mapping

2.3.3 More About Twists

2.3.4 Dual Quaternions

2.3.5 Configuration Space

2.4 Using the Toolbox

2.5 Wrapping Up

Further Reading

Exercises

3 Time and Motion

3.1 Time-Varying Pose

3.1.1 Derivative of Pose

3.1.2 Transforming Spatial Velocities

3.1.3 Incremental Rotation

3.1.4 Incremental Rigid-Body Motion

3.2 Accelerating Bodies and Reference Frames

3.2.1 Dynamics of Moving Bodies

3.2.2 Transforming Forces and Torques

3.2.3 Inertial Reference Frame

3.3 Creating Time-Varying Pose

3.3.1 Smooth One-Dimensional Trajectories

3.3.2 Multi-Dimensional Trajectories

3.3.3 Multi-Segment Trajectories

3.3.4 Interpolation of Orientation in 3D

3.3.4.1 Direction of Rotation

3.3.5 Cartesian Motion in 3D

3.4 Application: Inertial Navigation

l3.4.1 Gyroscopes

3.4.1.1 How Gyroscopes Work

3.4.1.2 Estimating Orientation

3.4.2 Accelerometers

3.4.2.1 How Accelerometers Work

3.4.2.2 Estimating Pose and Body Acceleration

3.4.3 Magnetometers

3.4.3.1 How Magnetometers Work

3.4.3.2 Estimating Heading

3.4.4 Sensor Fusion

3.5 Wrapping Up

Further Reading

Exercises

Part II Mobile Robots

II Mobile Robots

4 Mobile Robot Vehicles

4.1 Wheeled Mobile Robots

4.1.1 Car-Like Mobile Robots

4.1.1.1 Moving to a Point

4.1.1.2 Following a Line

4.1.1.3 Following a Trajectory

4.1.1.4 Moving to a Pose

4.1.2 Differentially-Steered Vehicle

4.1.3 Omnidirectional Vehicle

4.2 Flying Robots

4.3 Advanced Topics

4.3.1 Nonholonomic and Under-Actuated Systems

4.4Wrapping Up

Further Reading

Toolbox and MATLAB Notes

Exercises

Chapter

5 Navigation

5.1 Reactive Navigation

l5.1.1 Braitenberg Vehicles

l5.1.2 Simple Automata

5.2 Map-Based Planning

5.2.1 Distance Transform

5.2.2 D*

5.2.3 Introduction to Roadmap Methods

5.2.4 Probabilistic Roadmap Method (PRM)

5.2.5 Lattice Planner

5.2.6 Rapidly-Exploring Random Tree (RRT)

5.3 Wrapping Up

Further Reading

Resources

MATLAB Notes

Exercises

Chapter

6 Localization

6.1 Dead Reckoning

6.1.1 Modeling the Vehicle

6.1.2 Estimating Pose

6.2 Localizing with a Map

6.3 Creating a Map

6.4 Localization and Mapping

6.5 Rao-Blackwellized SLAM

6.6 Pose Graph SLAM

6.7 Sequential Monte-Carlo Localization

6.8 Application: Scanning Laser Rangefinder

Laser Odometry

Laser-Based Map Building

Laser-Based Localization

6.9 Wrapping Up

Further Reading

Toolbox and MATLAB Notes

Exercises

Part III Arm-Type Robots

III Arm-Type Robots

7 Robot Arm Kinematics

7.1 Forward Kinematics

7.1.1 2-Dimensional (Planar) Robotic Arms

7.1.2 3-Dimensional Robotic Arms

l7.1.2.1 Denavit-Hartenberg Parameters

l7.1.2.2 Product of Exponentials

l7.1.2.3 6-Axis Industrial Robot

7.2 Inverse Kinematics

7.2.1 2-Dimensional (Planar) Robotic Arms

7.2.1.1 Closed-Form Solution

7.2.1.2 Numerical Solution

7.2.2 3-Dimensional Robotic Arms

7.2.2.1Closed-Form Solution

7.2.2.2 Numerical Solution

7.2.2.3 Under-Actuated Manipulator

l7.2.2.4 Redundant Manipulator

7.3 Trajectories

7.3.1 Joint-Space Motion

7.3.2 Cartesian Motion

7.3.3 Kinematics in Simulink

7.3.4 Motion through a Singularity

7.3.5 Configuration Change

7.4 Advanced Topics

7.4.1 Joint Angle Offsets

7.4.2 Determining Denavit-Hartenberg Parameters

7.4.3 Modified Denavit-Hartenberg Parameters

7.5 Applications

7.5.1 Writing on a Surface

7.5.2 A Simple Walking Robot

Kinematics

Motion of One Leg

Motion of Four Legs

7.6 Wrapping Up

Further Reading

MATLAB and Toolbox Notes

Exercises

Chapter

8 Manipulator Velocity

8.1 Manipulator Jacobian

8.1.1 Jacobian in the World Coordinate Frame

8.1.2 Jacobian in the End-Effector Coordinate Frame

8.1.3 Analytical Jacobian

8.2 Jacobian Condition and Manipulability

l8.2.1 Jacobian Singularities

l8.2.2 Manipulability

8.3 Resolved-Rate Motion Control

8.3.1 Jacobian Singularity

8.4 Under- and Over-Actuated Manipulators

8.4.1 Jacobian for Under-Actuated Robot

8.4.2 Jacobian for Over-Actuated Robot

8.5 Force Relationships

8.5.1 Transforming Wrenches to Joint Space

8.5.2 Force Ellipsoids

8.6 Inverse Kinematics: a General Numerical Approach

l8.6.1 Numerical Inverse Kinematics

8.7 Advanced Topics

8.7.1 Computing the Manipulator Jacobian Using Twists

8.8 Wrapping Up

Further Reading

MATLAB and Toolbox Notes

Exercises

9 Dynamics and Control

9.1 Independent Joint Control

9.1.1 Actuators

9.1.2 Friction

9.1.3 Effect of the Link Mass

9.1.4 Gearbox

9.1.5 Modeling the Robot Joint

9.1.6 Velocity Control Loop

9.1.7 Position Control Loop

9.1.8 Independent Joint Control Summary

9.2 Rigid-Body Equations of Motion

9.2.1 Gravity Term

9.2.2 Inertia Matrix

9.2.3 Coriolis Matrix

9.2.4 Friction

9.2.5 Effect of Payload

9.2.6 Base Force

9.2.7 Dynamic Manipulability

9.3 Forward Dynamics

9.4 Rigid-Body Dynamics Compensation

9.4.1 Feedforward Control

9.4.2 Computed Torque Control

l9.4.3 Operational Space Control

9.5 Applications

9.5.1 Series-Elastic Actuator (SEA)

9.6 Wrapping Up

Further Reading

Exercises

Part IV Computer Vision

IV Computer Vision

10 Light and Color

10.1 Spectral Representation of Light

10.1.1 Absorption

10.1.2 Reflectance

10.1.3 Luminance

10.2 Color

10.2.1The Human Eye

10.2.2 Measuring Color

10.2.3 Reproducing Colors

10.2.4 Chromaticity Space

10.2.5 Color Names

10.2.6 Other Color and Chromaticity Spaces

10.2.7 Transforming between Different Primaries

10.2.8 What Is White?

10.3 Advanced Topics

10.3.1 Color Temperature

10.3.2 Color Constancy

10.3.3 White Balancing

10.3.4 Color Change Due to Absorption

10.3.5 Dichromatic Reflectance

10.3.6 Gamma

10.4 Application: Color Image

10.4.1 Comparing Color Spaces

10.4.2 Shadow Removal

10.5 Wrapping Up

Further Reading

Data Sources

Exercises

11 Image Formation

11.1 Perspective Camera

11.1.1 Perspective Projection

11.1.2 Modeling a Perspective Camera

11.1.3 Discrete Image Plane

11.1.4 Camera Matrix

11.1.5 Projecting Points

11.1.6 Lens Distortion

11.2 Camera Calibration

11.2.1 Homogeneous Transformation Approach

11.2.2 Decomposing the Camera Calibration Matrix

11.2.3 Pose Estimation

11.2.4 Camera Calibration Toolbox

11.3 Wide Field-of-View Imaging

11.3.1 Fisheye Lens Camera

11.3.2 Catadioptric Camera

11.3.3 Spherical Camera

11.4 Unified Imaging

11.4.1 Mapping Wide-Angle Images to the Sphere

11.4.2 Mapping from the Sphere to a Perspective Image

11.5 Novel Cameras

11.5.1 Multi-Camera Arrays

11.5.2 Light-Field Cameras

11.6 Advanced Topics

11.6.1 Projecting 3D Lines and Quadrics

11.6.2 Nonperspective Cameras

11.7 Wrapping Up

Further Reading and Resources

Toolbox Notes

Exercises

Chapter

12 Images and Image Processing

12.1 Obtaining an Image

12.1.1 Images from Files

12.1.2 Images from an Attached Camera

12.1.3 Images from a Movie File

12.1.4 Images from the Web

12.1.5 Images from Maps

12.1.6 Images from Code

12.2 Image Histograms

12.3 Monadic Operations

12.4 Diadic Operations

12.5 Spatial Operations

12.5.1 Linear Spatial Filtering

12.5.1.1 Smoothing

12.5.1.2 Boundary Effects

12.5.1.3 Edge Detection

12.5.2 Template Matching

12.5.2.1 Nonparameteric Local Transforms

12.5.3 Nonlinear Operations

12.6 Mathematical Morphology

12.6.1 Noise Removal

12.6.2 Boundary Detection

12.6.3 Hit or Miss Transform

12.6.4 Distance Transform

12.7 Shape Changing

12.7.1 Cropping

12.7.2 Image Resizing

12.7.3 Image Pyramids

12.7.4 Image Warping

12.8 Wrapping Up

Further Reading

Sources of Image Data

MATLAB Notes

General Software Tools

Exercises

Chapter

13 Image Feature Extraction

13.1 Region Features

13.1.1 Classification

13.1.1.1 Grey-Level Classification

13.1.1.2 Color Classification

13.1.2 Representation

13.1.2.1 Graph-Based Segmentation

13.1.3 Description

13.1.3.1 Bounding Boxes

13.1.3.2 Moments

13.1.3.3 Blob Features

13.1.3.4 Shape from Moments

13.1.3.5 Shape from Perimeter

13.1.3.6 Character Recognition

13.1.4 Summary

13.2 Line Features

13.2.1 Summary

13.3 Point Features

13.3.1 Classical Corner Detectors

13.3.2 Scale-Space Corner Detectors

13.3.2.1 Scale-Space Point Feature

13.4 Wrapping Up

MATLAB Notes

Further Reading

Exercises

Chapter

14 Using Multiple Images

14.1 Feature Correspondence

14.2 Geometry of Multiple Views

14.2.1 The Fundamental Matrix

14.2.2 The Essential Matrix

14.2.3 Estimating the Fundamental Matrix from Real Image Data

14.2.4 Planar Homography

14.3 Stereo Vision

14.3.1 Sparse Stereo

14.3.2 Dense Stereo Matching

14.3.2.1 Stereo Failure Modes

14.3.3 Peak Refinement

14.3.4 Cleaning up and Reconstruction

14.3.5 3D Texture Mapped Display

14.3.6 Anaglyphs

14.3.7 Image Rectification

14.4 Bundle Adjustment

14.5 Point Clouds

14.5.1 Fitting a Plane

14.5.2 Matching Two Sets of Points

14.6 Structured Light

14.7 Applications

14.7.1 Perspective Correction

14.7.2 Mosaicing

14.7.3 Image Matching and Retrieval

14.7.4 Visual Odometry

14.8 Wrapping Up

MATLAB and Toolbox Notes

Further Reading

Resources

Exercises

Part V Robotics, Vision and Control

V Robotics, Vision and Control

15 Vision-Based Control

15.1 Position-Based Visual Servoing

15.2 Image-Based Visual Servoing

15.2.1 Camera and Image Motion

15.2.2 Controlling Feature Motion

15.2.3 Estimating Feature Depth

15.2.4 Performance Issues

15.3 Using Other Image Features

15.3.1 Line Features

15.3.2 Circle Features

15.3.3 Photometric Features

15.4 Wrapping Up

Further Reading

Exercises

16 Advanced Visual Servoing

16.1 XY/Z-Partitioned IBVS

16.2 IBVS Using Polar Coordinates

16.3 IBVS for a Spherical Camera

16.4 Applications

16.4.1 Arm-Type Robot

16.4.2 Mobile Robot

16.4.2.1 Holonomic Mobile Robot

16.4.2.2 Nonholonomic Mobile Robot

16.4.3 Aerial Robot

16.5 Wrapping Up

Further Reading

Resources

Exercises

Appendices

A Installing the Toolboxes

B Linear Algebra Refresher

B.1 Vectors

B.2 Matrices

B.2.1 Square Matrices

B.2.2 Nonsquare and Singular Matrices

C Geometry

C.1 Euclidean Geometry

C.1.1 Points

C.1.2 Lines

C.1.2.1 Lines in 2D

C.1.2.2 Lines in 3D and Plücker Coordinates

C.1.3 Planes

C.1.4 Ellipses and Ellipsoids

C.1.4.1 Properties

C.1.4.2 Drawing an Ellipse

C.1.4.3 Fitting an Ellipse to Data

From a Set of Interior Points

From a Set of Boundary Points

C.2 Homogeneous Coordinates

C.2.1 Two Dimensions

C.2.1.1 Conics

C.2.2 Three Dimensions

C.2.2.1 Lines

C.2.2.2 Planes

C.2.2.3 Quadrics

C.3 Geometric Transformations

D Lie Groups and Algebras

E Linearization, Jacobians and Hessians

F Solving Systems of Equations

F.1 Linear Problems

F.1.1 Nonhomogeneous Systems

F.1.2 Homogeneous Systems

F.1.2 Homogeneous Systems

F.2.1 Finding Roots

F.2.2 Nonlinear Minimization

F.2.3 Nonlinear Least Squares Minimization

Numerical Issues

F.2.4 Sparse Nonlinear Least Squares

State Vector

Inherent Structure

Large Scale Problems

Anchoring

G Gaussian Random Variables

H Kalman Filter

H.1 Linear Systems – Kalman Filter

H.2 Nonlinear Systems – Extended Kalman Filter

I Graphs

Appendix

J Peak Finding

Bibliography

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