实例介绍
【实例简介】
高清，带完整书签。做机器人以及SLAM有两本圣经，一本是大名鼎鼎的《Multiple View Geometry in Computer Vision》，另一本就是至今虽然尚未出版，但是已经在SLAM界广为流传的《State Estimation for Robotics》，这本书深入讲解了李代数的理论，以及从滤波器的角度来深入分析了机器人的状态估计方法。高博鼎力推荐。
Revision history 13 May 2017 Version best matching published first edition 12Aug2017 Equation(4.47a):∑ r changed to∑x 12 Aug 2017 Page 111, bullet 4: 2y changed to >yy 12 Aug 2017 Page 117, bullet(ii): changed 4(a) to 3 12 Aug 2017 Equation (4.87): P- changed to P 12 Aug 2017 Equation(4.89 ): Pk changed to P, 12 Aug 2017 Equalion (4.102d): xop, k, o changed to xop, k 12 Aug 2017 Equation(7.102): removed negative sign 22 NoV 2017 Fixed typo in Jacobi identity(page 218 10 Dec 2017 Inline above(8.2): rii changed to r"iayD-1 10Dec2017F quation(2.52):∑-1 w2 ya changed to∑ 10 Dec 2017 Equation(6. 26 ):0 changed to 0 10 Dec 2017 Inline below (4 132):e(Xop )=Lu(Xop)changed to e(xo 10 Dec 2017 Angular acceleration: w21 changed to w 21 10 Dec 2017 Equation(4. 31): corrected a double conna 10 Dec 2017 Fquation(4.92a ) nk, i changed to yk, i Contents Acronyms and abbreviations otation Foreword 1 Introduction 1.1 A Littlc History 1.2 Sensors. Measurements and problem Definition 1.3 How This Book Is Organized v11345 1.4 Relationship to Other Books Part I Estimation Machinery 2 Primer on Probability Theory 2. 1 Probability density Functions 2.1.1 Definitions 79990 2.1.2 Bayes'Rule and Inference 2.1. 3 Moments 2.1.1 Sample Mean and Covariance 12 2.1.5 Statistically Independent, Uncorrelated 2.1.6 Normalized Product 2.1.7 Shannon and mutual Information 14 2.1.8 Cramer-Rao Lowcr Bound and Fisher Information 2.2 Gaussian Probability Density Functions 2.2.1 Definitions 15 2.2.2 Isserlis? Theorem 2.2.3 Joint gaussian pdfs, Their factors, and Inference 2.2.4 Statistically Independent, Uncorrelated 20 2.2.5 Linear Change of Varia bles 20 2.2.6 Normalized product of gaussians 22 2.2.7 Sherman-Morrison-Woodbury Identity 2.2.8 Passing a o 24 2.2.9 Shannon Information of a Gaussian 8 2.2.10 Mutual Information of a Joint Gaussian pdF 30 2.2.11 Cramer-Rao Lower Bound Applied to Gaussian PDFs 2.3 Gaussial Processes 32 2.1 Summary 33 Exercises Contents Linear-Gaussian estimation 3.1 Batch Discrete-Time Estimation 7 3.1.1 Problem se 3.1.2 Maximum a posterior :39 3.1.3B 3.1.4 Existence, Uniqueness, and Observability 46 3.1.5 MAP Covariance 50 3.2 Recursive Discrete- Time Smoothing 51 3.2.1 Exploiting Sparsity in the Batch Solution 52 3.2.2 Cholesky Smod 53 3.2.3 Rauch-Tung-Striebel Smoother 3.3 Recursive Discrete-Time Filtering 3.3.1Fz the Batch Soli 3.3.2 Kalman Filter via MAP 63 3.3.3 Kalman Filter via B 3.3.4 Kalman Filter via Gain Optinization 3.3.5 Kalman Filter Discussion 70 3.3.6 Error Dynam 71 3.3.7 Existence, Unique bilit 72 3.4 Batch Continuous-Timc Estimation 74 3.4.1 Gaussian Process regression 3.4.2 A Class of Exactly Sparse Gaussian Process Priors 77 3. 4.3 Linear Time-Invariant Case 3.4. 4 Relationship to Batch Discrete-Time stimation 87 3.5 Summar 3.6 Exercises 4 Nonlinear Non-Gaussian estimation 91 4.1 Introduct 91 4.1.1 Full Bayesian Estimation 92 4.1.2 Maxin 4.2 Recursive Discrete-Time estimation 1.2.1 Problem Setup 96 4.2. 2 Bavcs Filte 4.2.3 Extended Kalman Filtor 100 4.2. 4 Gcncralizcd Gaussian Filter 103 4.2.5 Iterated Extended Kalman Filter 105 4.2.6 EKF Is a maP estimator 106 4.2.7 Alternatives for Passing PDFs through nonlinearities 4.2.8 Particle filt 116 4.2.9Si int Kalman filt 2.10 Iterated Sigmapoint, Ka 4.2.11 ISPKF Seeks the Posterior mean 4.2.12 Taxonomy of Filters 4.3 Batch Discrete-Time estimation 127 4.3.1 Maximum A Posterior 128 4.3.2B 4. 3.3 Maximum Likelihood 4.34D 142 Contents 4.4 Batch continuous-Time estimation 143 4.4.1 Motion model 143 4.4.2 Observation model 146 4.4.3 Bayesian Inference 146 4.4.4 Algorithm Summary 147 4.5 ummar 148 4.6 Exercises 149 Biases. Correspondences, and Outliers 151 5. 1 Handling Input/Measurement Biases 152 5.1.1 Bias effects on the kalman filter 152 5.1.2 Unknown Input Bias 155 5.1.3 Unknown measurement bias 157 5.2 Data association 159 5.2.1 External Data Assuciation 160 5.2.2 Internal Data association 160 5.3 Handling Outliers 161 5.3.1 RANSAC 5.3.2M 163 Estimation 5.4 Summary 5 Part Ii Three-Dimensional Machinery 171 6 Primer on Thrcc-Dimcnsional Gcomctry 173 6.1 Vectors and Reference frames 1.1 Reference Frames 174 6.1.2 Dot Product 174 6.1.3 Cross Product 175 6.2 Rotations 176 6.2.1 Rotation matrices 176 6.2.2 Prin 6.2.3 Alternate Rotation Representations 178 6.2.4 Rotational kinematics 184 6.2.5 Perturbing rotations 188 6.3 Poses 192 6.3.1 Transformation Matrices 6.3.2 Robotics conventions 194 6..3上 renet- Serret上rame 6.4 Sensor models 199 6.4. G. 4.2 Stereo Camera 206 6.4.3R 208 4.4 Inertial Measurernent Unit 209 6.5 summarv 211 6.6 Exercises 212 Contents 7 Matrix Lie Groups 215 7.1 Geomety 215 7.1.1 Special Orthogonal and Special Euclidean Groups 215 7.1.2 Lie Algebras 217 7. 1. 3 Exponential Map 219 7. 1.4 Adjoints 226 7. 1. 5 Baker-Campbell-Hausdorff 230 7.1.6 Distance, Volume, Integration 237 7.1.7 Interpolation 240 1. 8 HoMogeneous points 246 7. 1.9 Calculus and Optimization 7.1.10 Identitics 254 7.2 Kinema.tics 7.2.1 Rotations 255 7. 2.2 Poses 258 7.2.3 Linearized Rotations 261 7.2.1 Linearized p 265 7.3 Probability and statistics 266 7.3.1 Gaussian Random Varia bles and pips 7.3.2U 7.3.3C 273 7.3.4 Fusing Pose 7.3.5 Propagating Uncertainty through a Nonlincar Camera Model 285 7.4S1 292 7.5 Exercises 293 Part Ili Applications 8 Pose estimation problems 297 8.1 Point-Cloud Alignment 297 8. 1.1 Problcm Sc 8.1.2 Unit-Length Quaternion Solution 298 8. 1.3 Rotation Matrix Solution 02 atrix solution 316 8.2 Point-Cloud Tracki 319 8.2.1 Problem Se 319 8.2.2 Motion priors 320 8.2.3 Measurement model 321 8.2.4 kF Solution 722 8.2.5 Batch Maximum a Posteriori solution 8.3 Pose-Graph relaxation 329 8.3.1 Problem Set 8.3.2 Batch Maximum Likelihood Solution 330 8.3.3 Initialization 33 8.3.4 Exploiting Sp 33 8.3.5 Chain Exanple 334 Contents IX Pose-and-Point estimation problems 337 9. 1 Bundle Adjustment 37 9.1.1 Problem Setup 9.1.2 Measurement Model 9.1.3 Maximum Likelihood Solution 342 9.1.4 Exploiting Sparsity 345 9.1.5 Interpolation Example 348 9.2 Simultaneous Localization and Mapping 352 9.2.1 Problem Setup 9.2.2 Batch Maximum a Posteriori Solution 353 9.2.3 Exploiting Sparsity 354 9.2.4 Example 355 10 Continuous-Time estimation 357 10.1 Motion prior 57 357 10.1.2 Simplifica tion 361 10.2 Simultaneous trajectory Estimation and Mapping 362 10.2.1 Problem Setup 363 10.2.2 Measurement Model 363 10.2.3 Batch Maximum a Posteriori solution 364 10.2.4 xploiting Sparsity 365 10.2.5 Interpolation 366 10.2.6Pc 367 References 369 Inde 375 【实例截图】
【核心代码】
高清，带完整书签。做机器人以及SLAM有两本圣经，一本是大名鼎鼎的《Multiple View Geometry in Computer Vision》，另一本就是至今虽然尚未出版，但是已经在SLAM界广为流传的《State Estimation for Robotics》，这本书深入讲解了李代数的理论，以及从滤波器的角度来深入分析了机器人的状态估计方法。高博鼎力推荐。
Revision history 13 May 2017 Version best matching published first edition 12Aug2017 Equation(4.47a):∑ r changed to∑x 12 Aug 2017 Page 111, bullet 4: 2y changed to >yy 12 Aug 2017 Page 117, bullet(ii): changed 4(a) to 3 12 Aug 2017 Equation (4.87): P- changed to P 12 Aug 2017 Equation(4.89 ): Pk changed to P, 12 Aug 2017 Equalion (4.102d): xop, k, o changed to xop, k 12 Aug 2017 Equation(7.102): removed negative sign 22 NoV 2017 Fixed typo in Jacobi identity(page 218 10 Dec 2017 Inline above(8.2): rii changed to r"iayD-1 10Dec2017F quation(2.52):∑-1 w2 ya changed to∑ 10 Dec 2017 Equation(6. 26 ):0 changed to 0 10 Dec 2017 Inline below (4 132):e(Xop )=Lu(Xop)changed to e(xo 10 Dec 2017 Angular acceleration: w21 changed to w 21 10 Dec 2017 Equation(4. 31): corrected a double conna 10 Dec 2017 Fquation(4.92a ) nk, i changed to yk, i Contents Acronyms and abbreviations otation Foreword 1 Introduction 1.1 A Littlc History 1.2 Sensors. Measurements and problem Definition 1.3 How This Book Is Organized v11345 1.4 Relationship to Other Books Part I Estimation Machinery 2 Primer on Probability Theory 2. 1 Probability density Functions 2.1.1 Definitions 79990 2.1.2 Bayes'Rule and Inference 2.1. 3 Moments 2.1.1 Sample Mean and Covariance 12 2.1.5 Statistically Independent, Uncorrelated 2.1.6 Normalized Product 2.1.7 Shannon and mutual Information 14 2.1.8 Cramer-Rao Lowcr Bound and Fisher Information 2.2 Gaussian Probability Density Functions 2.2.1 Definitions 15 2.2.2 Isserlis? Theorem 2.2.3 Joint gaussian pdfs, Their factors, and Inference 2.2.4 Statistically Independent, Uncorrelated 20 2.2.5 Linear Change of Varia bles 20 2.2.6 Normalized product of gaussians 22 2.2.7 Sherman-Morrison-Woodbury Identity 2.2.8 Passing a o 24 2.2.9 Shannon Information of a Gaussian 8 2.2.10 Mutual Information of a Joint Gaussian pdF 30 2.2.11 Cramer-Rao Lower Bound Applied to Gaussian PDFs 2.3 Gaussial Processes 32 2.1 Summary 33 Exercises Contents Linear-Gaussian estimation 3.1 Batch Discrete-Time Estimation 7 3.1.1 Problem se 3.1.2 Maximum a posterior :39 3.1.3B 3.1.4 Existence, Uniqueness, and Observability 46 3.1.5 MAP Covariance 50 3.2 Recursive Discrete- Time Smoothing 51 3.2.1 Exploiting Sparsity in the Batch Solution 52 3.2.2 Cholesky Smod 53 3.2.3 Rauch-Tung-Striebel Smoother 3.3 Recursive Discrete-Time Filtering 3.3.1Fz the Batch Soli 3.3.2 Kalman Filter via MAP 63 3.3.3 Kalman Filter via B 3.3.4 Kalman Filter via Gain Optinization 3.3.5 Kalman Filter Discussion 70 3.3.6 Error Dynam 71 3.3.7 Existence, Unique bilit 72 3.4 Batch Continuous-Timc Estimation 74 3.4.1 Gaussian Process regression 3.4.2 A Class of Exactly Sparse Gaussian Process Priors 77 3. 4.3 Linear Time-Invariant Case 3.4. 4 Relationship to Batch Discrete-Time stimation 87 3.5 Summar 3.6 Exercises 4 Nonlinear Non-Gaussian estimation 91 4.1 Introduct 91 4.1.1 Full Bayesian Estimation 92 4.1.2 Maxin 4.2 Recursive Discrete-Time estimation 1.2.1 Problem Setup 96 4.2. 2 Bavcs Filte 4.2.3 Extended Kalman Filtor 100 4.2. 4 Gcncralizcd Gaussian Filter 103 4.2.5 Iterated Extended Kalman Filter 105 4.2.6 EKF Is a maP estimator 106 4.2.7 Alternatives for Passing PDFs through nonlinearities 4.2.8 Particle filt 116 4.2.9Si int Kalman filt 2.10 Iterated Sigmapoint, Ka 4.2.11 ISPKF Seeks the Posterior mean 4.2.12 Taxonomy of Filters 4.3 Batch Discrete-Time estimation 127 4.3.1 Maximum A Posterior 128 4.3.2B 4. 3.3 Maximum Likelihood 4.34D 142 Contents 4.4 Batch continuous-Time estimation 143 4.4.1 Motion model 143 4.4.2 Observation model 146 4.4.3 Bayesian Inference 146 4.4.4 Algorithm Summary 147 4.5 ummar 148 4.6 Exercises 149 Biases. Correspondences, and Outliers 151 5. 1 Handling Input/Measurement Biases 152 5.1.1 Bias effects on the kalman filter 152 5.1.2 Unknown Input Bias 155 5.1.3 Unknown measurement bias 157 5.2 Data association 159 5.2.1 External Data Assuciation 160 5.2.2 Internal Data association 160 5.3 Handling Outliers 161 5.3.1 RANSAC 5.3.2M 163 Estimation 5.4 Summary 5 Part Ii Three-Dimensional Machinery 171 6 Primer on Thrcc-Dimcnsional Gcomctry 173 6.1 Vectors and Reference frames 1.1 Reference Frames 174 6.1.2 Dot Product 174 6.1.3 Cross Product 175 6.2 Rotations 176 6.2.1 Rotation matrices 176 6.2.2 Prin 6.2.3 Alternate Rotation Representations 178 6.2.4 Rotational kinematics 184 6.2.5 Perturbing rotations 188 6.3 Poses 192 6.3.1 Transformation Matrices 6.3.2 Robotics conventions 194 6..3上 renet- Serret上rame 6.4 Sensor models 199 6.4. G. 4.2 Stereo Camera 206 6.4.3R 208 4.4 Inertial Measurernent Unit 209 6.5 summarv 211 6.6 Exercises 212 Contents 7 Matrix Lie Groups 215 7.1 Geomety 215 7.1.1 Special Orthogonal and Special Euclidean Groups 215 7.1.2 Lie Algebras 217 7. 1. 3 Exponential Map 219 7. 1.4 Adjoints 226 7. 1. 5 Baker-Campbell-Hausdorff 230 7.1.6 Distance, Volume, Integration 237 7.1.7 Interpolation 240 1. 8 HoMogeneous points 246 7. 1.9 Calculus and Optimization 7.1.10 Identitics 254 7.2 Kinema.tics 7.2.1 Rotations 255 7. 2.2 Poses 258 7.2.3 Linearized Rotations 261 7.2.1 Linearized p 265 7.3 Probability and statistics 266 7.3.1 Gaussian Random Varia bles and pips 7.3.2U 7.3.3C 273 7.3.4 Fusing Pose 7.3.5 Propagating Uncertainty through a Nonlincar Camera Model 285 7.4S1 292 7.5 Exercises 293 Part Ili Applications 8 Pose estimation problems 297 8.1 Point-Cloud Alignment 297 8. 1.1 Problcm Sc 8.1.2 Unit-Length Quaternion Solution 298 8. 1.3 Rotation Matrix Solution 02 atrix solution 316 8.2 Point-Cloud Tracki 319 8.2.1 Problem Se 319 8.2.2 Motion priors 320 8.2.3 Measurement model 321 8.2.4 kF Solution 722 8.2.5 Batch Maximum a Posteriori solution 8.3 Pose-Graph relaxation 329 8.3.1 Problem Set 8.3.2 Batch Maximum Likelihood Solution 330 8.3.3 Initialization 33 8.3.4 Exploiting Sp 33 8.3.5 Chain Exanple 334 Contents IX Pose-and-Point estimation problems 337 9. 1 Bundle Adjustment 37 9.1.1 Problem Setup 9.1.2 Measurement Model 9.1.3 Maximum Likelihood Solution 342 9.1.4 Exploiting Sparsity 345 9.1.5 Interpolation Example 348 9.2 Simultaneous Localization and Mapping 352 9.2.1 Problem Setup 9.2.2 Batch Maximum a Posteriori Solution 353 9.2.3 Exploiting Sparsity 354 9.2.4 Example 355 10 Continuous-Time estimation 357 10.1 Motion prior 57 357 10.1.2 Simplifica tion 361 10.2 Simultaneous trajectory Estimation and Mapping 362 10.2.1 Problem Setup 363 10.2.2 Measurement Model 363 10.2.3 Batch Maximum a Posteriori solution 364 10.2.4 xploiting Sparsity 365 10.2.5 Interpolation 366 10.2.6Pc 367 References 369 Inde 375 【实例截图】
【核心代码】
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