Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building

Juan Andrade Cetto, Alberto Sanfeliu9783540327950, 3-540-32795-9

This monograph covers theoretical aspects of simultaneous localization and map building for mobile robots, such as estimation stability, nonlinear models for the propagation of uncertainties, temporal landmark compatibility, as well as issues pertaining the coupling of control and SLAM. One of the most relevant topics covered in this monograph is the theoretical formalism of partial observability in SLAM. The authors show that the typical approach to SLAM using a Kalman filter results in marginal filter stability, making the final reconstruction estimates dependant on the initial vehicle estimates. However, by anchoring the map to a fixed landmark in the scene, they are able to attain full observability in SLAM, with reduced covariance estimates. This result earned the first author the EURON Georges Giralt Best PhD Award in its fourth edition, and has prompted the SLAM community to think in new ways to approach the mapping problem. For example, by creating local maps anchored on a landmark, or on the robot initial estimate itself, and then using geometric relations to fuse local maps globally. This monograph is appropriate as a text for an introductory estimation-theoretic approach to the SLAM problem, and as a reference book for people who work in mobile robotics research in general.

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Table of contents :
front-matter.pdf……Page 1
001-047.pdf……Page 13
1.1 Extended Kalman Filter Approach to SLAM……Page 16
1.2 Mobile Robot Platforms……Page 29
1.3 Temporal Landmark Validation……Page 32
1.4 Performance of EKF SLAM with Landmark Validation……Page 39
1.6 Bibliographical Notes……Page 55
1.7 Concluding Remarks……Page 59
049-084.pdf……Page 60
2.1 Steady State Behavior of EKF-SLAM……Page 61
2.2 Total Fisher Information……Page 63
2.3 Convergence……Page 66
2.4 Observable and Controllable Subspace……Page 67
2.5 The Monobot……Page 68
2.6 The Planar Robot……Page 76
2.7 Observability……Page 80
2.8 Controllabilit……Page 91
2.10 Conclusions……Page 95
085-096.pdf……Page 96
3.2 O ( n ) and Stable Partially Observable SLAM……Page 98
3.3 O ( n ) and Stable Fully Observable SLAM……Page 99
3.4 Experimental Results……Page 103
097-106.pdf……Page 108
4.1 Nonlinear Propagation of State Estimates……Page 109
4.2 UT of Vehicle States……Page 110
4.3 Experimental Results. EKF, UKF, and Vehicle-Only UT……Page 112
4.4 Conclusion……Page 113
107-118.pdf……Page 118
5.1 Linear Quadratic Gaussian Regulation……Page 119
5.2 The EKF for Multirobot SLAM……Page 123
5.3 Feedback Linearization……Page 124
5.4 Conclusions……Page 127
Recursive State Estimation……Page 130
Linear Kalman Filter……Page 131
Extended Kalman Filter……Page 133
Conditioning……Page 134
Sequential Innovation……Page 135
Bibliographical Notes……Page 136
127-128.pdf……Page 137
129-130.pdf……Page 139
back-matter.pdf……Page 141