PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

icg.tugraz.at

PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

Contents

1 Introduction to mobile robotics and vision 1

1.1 Localization and map building in mobile robotics . . . . . . . . . . . . . . . . . . 2

1.2 Why vision? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 What has already been achieved? . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Why is it hard? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5 How can it get solved? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.6 Contribution of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.7 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Visual localization 11

2.1 Localization in metric maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Localization from point features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Localization from line features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Localization from plane features . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Local detectors 20

3.1 Interest point detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1.1 Harris detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1.2 Hessian detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2 Scale invariant detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.1 Scale-invariant Harris detector . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2.2 Scale-invariant Hessian detector . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2.3 Difference of Gaussian detector (DOG) . . . . . . . . . . . . . . . . . . . 26

3.2.4 Salient region detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2.5 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.3 Affine invariant detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3.1 Affine-invariant Harris detector . . . . . . . . . . . . . . . . . . . . . . . . 31

3.3.2 Affine-invariant Hessian detector . . . . . . . . . . . . . . . . . . . . . . . 33

3.3.3 Maximally stable region detector (MSER) . . . . . . . . . . . . . . . . . . 33

3.3.4 Affine-invariant salient region detector . . . . . . . . . . . . . . . . . . . . 34

3.3.5 Intensity extrema-based region detector (IBR) . . . . . . . . . . . . . . . 36

3.3.6 Edge based region detector (EBR) . . . . . . . . . . . . . . . . . . . . . . 37

3.3.7 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.4 Comparison of the described methods . . . . . . . . . . . . . . . . . . . . . . . . 40

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