Stereo SLAM. Davide Migliore, PhD Department of Electronics and Information, Politecnico di Milano, Italy
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1 Stereo SLAM, PhD Department of Electronics and Information, Politecnico di Milano, Italy
2 What is a Stereo Camera? Slide n 2 Do you remember the pin-hole camera?
3 What is a Stereo Camera? Slide n 3 Two cameras that perceive the world - Each camera has a P matrix
4 What is a Stereo Camera? Slide n 4 Two cameras that perceive the world
5 What is a Stereo Camera? Slide n 5 Two cameras that perceive the world
6 What is a Stereo Camera? Slide n 6 Two cameras that perceive the world
7 What is a Stereo Camera? Slide n 7 Error modeling problem
8 Stereo SLAM (Paz et al. 2008) Slide n 8 The idea - Use the Unified Inverse Depth parametrization (Montiel et al. 2006) - Rectify images and initialize the point using
9 Stereo SLAM (Paz et al. 2008) Slide n 9 Measurement Equations
10 Stereo SLAM (Paz et al. 2008) Slide n 10 Measurement Equations
11 Stereo SLAM (Paz et al. 2008) Slide n 11
12 Classic EKF SLAM Slide n 12 PhD - migliore@elet.polimi.it
13 Classic EKF SLAM Slide n 12 Extended Kalman Filter Video Frame PhD - migliore@elet.polimi.it
14 Classic EKF SLAM Slide n 12 Extended Kalman Filter FD Feature Detection Feature Initialization Prediction Video Frame Update SLAM Filter PhD - migliore@elet.polimi.it
15 Classic EKF SLAM Slide n 12 Extended Kalman Filter FD Feature Detection Feature Initialization Prediction Video Frame Update SLAM Filter PhD - migliore@elet.polimi.it
16 Classic EKF SLAM Slide n 12 Extended Kalman Filter FD Feature Detection Feature Initialization Prediction Data Association Video Frame DA Update SLAM Filter PhD - migliore@elet.polimi.it
17 Classic EKF SLAM Slide n 12 Extended Kalman Filter FD Feature Detection Feature Initialization Prediction Data Association Video Frame DA Update SLAM Filter PhD - migliore@elet.polimi.it
18 Stereo SLAM (Paz et al. 2008) Slide n 13 Data Association Trouble
19 Stereo SLAM (Paz et al. 2008) Slide n 14 Data Association Trouble
20 Stereo SLAM (Paz et al. 2008) Slide n 15 Data Association Trouble
21 Compatibility Slide n 16
22 NN Data Association Slide n 17
23 NN Data Association Slide n 18
24 Joint Compatibility Slide n 19
25 JCBB Slide n 20
26 JCBB Slide n 21
27 Demo Time Slide n 22 Switch on Matlab
28 Stereo SLAM (Paz et al. 2008) Slide n 23 Joint Compatibility Branch & Bound Results
29 Stereo SLAM (Paz et al. 2008) Slide n 24 Results
30 Scaling problem Slide n 25
31 Scaling problem Slide n 26 O(n 2 )
32 Solution: local maps Slide n 27 Switch to matlab again
33 Stereo SLAM (Paz et al. 2008) Slide n 28 Results
34 Stereo SLAM (Paz et al. 2008) Slide n 29 Results
35 Stereo SLAM (Tomono 2009) Slide n 30 Results
36 Stereo SLAM (Tomono 2009) Slide n 30 Results
37 Stereo SLAM (Tomono 2009) Slide n 31 Results
38 Stereo SLAM (Tomono 2009) Slide n 32 Results
39 Inverse Scaling? Slide n 33 Is it possible to use the inverse scaling? Yes Results? Coming soon!!
40 Thanks for your attention Slide n 34 PhD - migliore@elet.polimi.it
41 Thanks for your attention Slide n 34 Questions PhD - migliore@elet.polimi.it
42 Omnidirectional SLAM, PhD Department of Electronics and Information, Politecnico di Milano, Italy
43 What is an Omni Camera? Slide n Omnidirectional sensors come in many varieties, but by definition must have a wide field-of-view. ~180º FOV ~360º FOV >180º FOV wide FOV dioptric cameras (e.g. fisheye) catadioptric cameras (e.g. cameras and mirror systems) polydioptric cameras (e.g. multiple overlapping cameras)
44 (Poly-)Dioptric solutions Slide n One to two fish-eye cameras or many synchornized cameras Pros: - High resolution per viewing angle Cons: - Bandwidth - Multiple cameras
45 (Poly-)Dioptric solutions Slide n One to two fish-eye cameras or many synchornized cameras Homebrewed polydioptric cameras are cheaper, but require calibrating and synchronizing; commercial designs tend to be expensive
46 Catadioptric solutions Slide n Usually single camera combined with convex mirror Pros: - Single image Cons: - Blind spots - Low resolution
47 Camera Models Slide n 40 Perspective camera Image plane (CCD) Single effective viewpoint
48 Camera Models Slide n 40 Perspective camera Image plane (CCD) Single effective viewpoint
49 Camera Models Slide n 40 Perspective camera Image plane (CCD) Single effective viewpoint
50 Camera Models Slide n 40 Perspective camera Image plane (CCD) Single effective viewpoint
51 Camera Models Slide n Catadioptric cameras
52 Camera Models Slide n Catadioptric cameras mirror
53 Camera Models Slide n Catadioptric cameras mirror perspective camera
54 Camera Models Slide n Catadioptric cameras mirror perspective camera
55 Camera Models Slide n Catadioptric cameras mirror perspective camera
56 Camera Models Slide n Catadioptric cameras mirror perspective camera
57 Camera Models Slide n Catadioptric cameras mirror perspective camera
58 Camera Models Slide n Central catadioptric cameras mirror camera
59 Camera Models Slide n Central catadioptric cameras mirror camera single effective viewpoint
60 Camera Models Slide n Central catadioptric cameras mirror (surface of revolution of a conic) camera single effective viewpoint
61 Types of central catadioptric cameras Slide n 43 F1 F2
62 Types of central catadioptric cameras Slide n 43 hyperbola + perspective camera F1 F2
63 Types of central catadioptric cameras Slide n 43 hyperbola + perspective camera parabola + orthographic lens F1 F1 F2
64 Types of central catadioptric cameras Slide n 43 hyperbola + perspective camera parabola + orthographic lens F1 F1 F2
65 Types of central catadioptric cameras Slide n 43 hyperbola + perspective camera parabola + orthographic lens F1 F1 F2
66 Types of central catadioptric cameras Slide n 43 hyperbola + perspective camera parabola + orthographic lens... F1 F1 F2
67 Other types of central cameras Slide n 44
68 Other types of central cameras Slide n 44
69 Why do we need calibration? Slide n 45 Z Y X p = v u
70 Why do we need calibration? Slide n 45 Calibration gives the relation between 2D & 3D Z For each pixel 3D vector emanating from the single viewpoint X Y p = u v
71 Why do we need calibration? Slide n 45 Calibration gives the relation between 2D & 3D Z For each pixel 3D vector emanating from the single viewpoint X Y p = u v
72 Why do we need calibration? Slide n 45 Calibration gives the relation between 2D & 3D Z For each pixel 3D vector emanating from the single viewpoint X Y p = u v
73 Why do we need calibration? Slide n 45 Calibration gives the relation between 2D & 3D Z For each pixel 3D vector emanating from the single viewpoint X Y p = u v
74 Why do we need calibration? Slide n 45 Calibration gives the relation between 2D & 3D Z For each pixel 3D vector emanating from the single viewpoint X Y p = u v
75 What? Slide n Z X Y u v
76 What? Slide n Center of the omnidirectional image Z X Y u v
77 What? Slide n Center of the omnidirectional image Camera focal length Z X Y v u Focal length
78 What? Slide n Center of the omnidirectional image Camera focal length Orientation and position between camera & mirror Z X Y R, T Focal length u v
79 What? Slide n Center of the omnidirectional image Camera focal length Orientation and position between camera & mirror Mirror shape Z X Y R, T Focal length u v
80 Assumptions Slide n Z X Y R, T u v Focal length
81 Assumptions Slide n 1. Mirror and camera axes are aligned => Z X Y R, T u v Focal length
82 Assumptions Slide n 1. Mirror and camera axes are aligned => Z X Y R, T u v Focal length
83 Assumptions Slide n 1. Mirror and camera axes are aligned => Z X Y 2. x-y mirror axes coincide with u-v camera axes => R, T u v Focal length
84 And how about non-central cameras? Slide n Reflected rays do not intersect in a point but are tangent to a caustic
85 And how about non-central cameras? Slide n Reflected rays do not intersect in a point but are tangent to a caustic
86 Visual Odometry (Scaramuzza et al. 2009) Slide n 49
87 Omni SFM (Lhuillier et al. 2008) Slide n 50
88 Omni SFM (Lhuillier et al. 2008) Slide n 51
89 Omni SFM (Lhuillier et al. 2008) Slide n 52
90 Thanks for your attention Slide n 53 PhD - migliore@elet.polimi.it
91 Thanks for your attention Slide n 53 Questions PhD - migliore@elet.polimi.it
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