Accurate Real-Time Disparity Map Computation Based on Variable Support Window
Map Inference in the Face of Noise and Disparity€¦ · Map Inference in the Face of Noise and...
Transcript of Map Inference in the Face of Noise and Disparity€¦ · Map Inference in the Face of Noise and...
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Map Inference in theFace of Noise and DisparityJames Biagioni and Jakob Eriksson
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Map making
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Opportunistic data collection
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Opportunistic data collection
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Making maps is hard
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“Turn left and drive off thebridge onto the 280”
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slide 9
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A couple of questions...
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Why infer maps?
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Road surveys
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Rural/developing areas
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New road construction
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Road closures
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Road closures
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Are noise and disparityactually problems?
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Current state of the art‣ k-Means clustering
- Edelkamp & Schrödl (2003)
‣ Kernel density estimation- Davies et al. (2006)
‣ Trace merging- Cao & Krumm (2009)
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Noisy, disparate GPS data
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Edelkamp & Schrödl (2003)
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Davies et al. (2006)
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Davies et al. (2006)
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Cao & Krumm (2009)
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Lessons learned1) KDE method looks promising
- Noise resistant, robust centerline extraction
2) Not without its problems- Threshold selection, single centerline
3) Trajectory data is valuable- Road geometry and topology
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A hybrid approach
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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A hybrid approach
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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Map inference pipeline
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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Raw GPS traces
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2-D histogram
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Density estimate
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Map inference pipeline
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Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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Density estimate
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Apply (high) threshold
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Binary skeletonization
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Generated map
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Generated map
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Apply (low) threshold
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Binary skeletonization
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Generated map
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Grayscale skeletonization
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1) Threshold
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2) Skeletonize
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3) “Lock in”
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‣
1) Threshold
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2) Skeletonize
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3) “Lock in”
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1) Threshold
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2) Skeletonize
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3) “Lock in”
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1) Threshold
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2) Skeletonize
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3) “Lock in”
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1) Threshold
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2) Skeletonize
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3) “Lock in”
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slide
1) Threshold
64
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slide
2) Skeletonize
65
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slide
3) “Lock in”
66
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slide
1) Threshold
67
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slide
2) Skeletonize
68
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slide
3) “Lock in”
69
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slide
Initial generated map
70
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slide
Map inference pipeline
71
Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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slide
Raw GPS traces
72
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slide
Initial generated map
73
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slide
Uniform transition probabilities
74
0.33
0.33
0.33
‣ HMM-based map-matching- Based on VTrack (Thiagarajan et al., 2009)
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slide
Density weighted roads
75
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slide
0.50
0.35
0.15
Weighted transition probabilities
76
‣ HMM-based map-matching- Based on VTrack (Thiagarajan et al., 2009)
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slide
Map inference pipeline
77
Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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Trace goodness of fit
78
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slide
Trace goodness of fit
79
RMSD(⌧, e) =
s1
|⌧ |X
p2⌧
dist(p, e)2
RMSD(⌧, e) < RMSDmax
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slide
Map before pruning
80
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slide
Map after pruning
81
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slide
Orphaned road segments
82
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slide
Road segment “spurs”
83
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slide
Map after pruning... again
84
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slide
Map after pruning
85
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slide
Incorrect topology
86
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slide
Collapsed intersection
87
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slide
Map inference pipeline
88
Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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slide
Simple geometry
89
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slide
Initial cluster locations
90
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slide
Settled cluster locations
91
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slide
Refined lane geometry
92
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slide
Intersection refinement
93
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slide
Intersection refinement
94
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slide
Intersection refinement
95
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slide
Refined intersection geometry
96
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slide
Map inference pipeline
97
Density Estimation
Initial Map Generation
Trace Map Matching
Topology Refinement
Geometry Refinement
Raw GPS traces
Final map
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slide 98
Quantitative Evaluation
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slide 99
Evaluation metrics‣ Geometric evaluation
- Liu et al. (2012)
‣ Topological evaluation- Biagioni & Eriksson (2012)
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slide
Ground truth segment
100
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slide
Ground truth samples
101
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slide
Inferred map segment
102
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slide
Inferred map samples
103
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slide
Samples compared
104
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slide
Matching threshold
≤ m? ≤ m? ≤ m? ≤ m?
105
m = matching threshold
≤ m? ≤ m? ≤ m? ≤ m?
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slide
≤ m? ≤ m? ≤ m? ≤ m?
Matching threshold
106
m = matching threshold
✗ ✗✓ ✓
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slide
≤ m? ≤ m? ≤ m? ≤ m?
“Missing”
107
m = matching threshold
✗ ✗✓ ✓
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slide
≤ m? ≤ m? ≤ m? ≤ m?
“Spurious”
108
m = matching threshold
✗ ✗✓ ✓
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slide
Overall performance
109
precision = 1� |spurious samples||inferred samples|
recall = 1� |missing samples||ground truth samples|
F = 2 · precision · recallprecision+ recall
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Geometric results
110
0.4
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30
F-sc
ore
Matching Threshold (m)
Biagioni & ErikssonCao
EdelkampDavies
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slide
Topological results
111
0.4
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30
F-sc
ore
Matching Threshold (m)
Biagioni & ErikssonCao
EdelkampDavies
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slide
Precision/recall
112
0.4
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30Matching Threshold (m)
F-score Precision Recall
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slide 113
Qualitative Evaluation
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slide 114
Edelkamp & Schrödl (2003)
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slide 115
Edelkamp & Schrödl (2003)
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slide 116
Edelkamp & Schrödl (2003)
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slide 117
Davies et al. (2006)
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slide 118
Davies et al. (2006)
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slide 119
Cao & Krumm (2009)
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slide 120
Cao & Krumm (2009)
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slide 121
Cao & Krumm (2009)
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slide 122
Biagioni & Eriksson (2012)
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slide 123
Biagioni & Eriksson (2012)
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slide 124
Biagioni & Eriksson (2012)
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slide
Future work
125
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Questions?Thanks!
Source code and data availablehttp://bits.cs.uic.edu