3D Robotic Mapping: The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom

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Springer Science & Business Media, Jan 17, 2009 - Technology & Engineering - 204 pages
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Focuses on acquiring spatial models of physical environments through mobile robots

The robotic mapping problem is commonly referred to as SLAM (simultaneous localization and mapping).

3D maps are necessary to avoid collisions with complex obstacles and to self-localize in six degrees of freedom (x-, y-, z-position, roll, yaw and pitch angle)

New solutions to the 6D SLAM problem for 3D laser scans are proposed and a wide variety of applications are presented

 

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Contents

Introduction
1
11 A Brief Introduction to Robotic Mapping
2
12 The Big Picture
7
Perceiving 3D Range Data
8
212 PhaseShift Measurement
10
213 Triangulation
11
223 3D Laser Scanner
12
224 Projection 3D Scanner
14
612 The Mobile Robot Kurt3D
110
613 Full 6D SLAM in a Planar Environment
111
614 Full 6D SLAM in an IndoorOutdoor Environment
112
615 Full 6D SLAM in an Outdoor Environment
114
616 Kurt3D Competitions
121
62 Globally Consistent Registration of High Resolution Scans
123
63 Mapping Urban Environments Registration with Dynamic Network Construction
125
64 Benchmarking 6D SLAM
130

23 Cameras and Camera Models
15
231 The Pinhole Camera Model and Perspective Projection
16
232 Stereo Cameras
19
24 3D Cameras
26
State of the Art
29
312 Planar 3D Mapping
31
313 SliceWise 6D SLAM
32
32 Globally Consistent Range Image Alignment
33
3D Range Image Registration
34
411 Direct Solutions of the ICP Error Function
36
412 Approximate Solution of the ICP Error Function by a Helical Motion
48
413 Linearized Solution of the ICP Error Function
50
414 Computing Closest Points
52
415 Implementation Issues
64
416 The Parallel ICP Algorithm
67
42 Evaluation of the ICP Algorithm
69
Globally Consistent Range Image Registration
77
512 Closed Loop Detection
78
513 Error Diffusion
79
52 GraphSLAM as Generalization of Loop Closing
81
523 Transforming the Solution
87
53 Other Modeling Approaches
93
532 GraphSLAM Using Helical Motion
99
533 GraphSLAM Using Linearization of a Rotation
104
Experiments and Results
109
641 Ground Truth Experiments
133
642 The Benchmarking Technique
134
643 Experimental Results
138
644 Justification of the Results
141
65 Further Applications
145
652 Applications in Medicine
151
3D Maps with Semantics
155
712 Interpretation
156
Model Refinement
160
72 Object Localization in 3D Data
163
722 Object Localization
169
73 Semantic 3D Maps
170
Conclusions and Outlook
173
Appendix A Math Appendix
176
A12 Axis Angle
178
A13 Unit Quaternions
179
A14 Converting Rotation Representations
181
A2 Plucker Coordinates
182
A3 Additional Theorems from Linear Algebra
183
A4 Solving Linear Systems of Equations
186
A5 Computing Constrained Extrema
187
A51 Computing the Minimum of a Quadratic Form with Subject to Conditions
188
A6 Numerical Function Minimization
190
References
193
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