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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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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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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Initial generated 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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Initial generated map
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Uniform transition probabilities
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0.33
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‣ HMM-based map-matching- Based on VTrack (Thiagarajan et al., 2009)
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Density weighted roads
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Weighted transition probabilities
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‣ HMM-based map-matching- Based on VTrack (Thiagarajan et al., 2009)
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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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Trace goodness of fit
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Trace goodness of fit
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RMSD(⌧, e) =
s1
|⌧ |X
p2⌧
dist(p, e)2
RMSD(⌧, e) < RMSDmax
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Map before pruning
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Map after pruning
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Orphaned road segments
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Road segment “spurs”
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Map after pruning... again
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Map after pruning
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Incorrect topology
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Collapsed intersection
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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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Simple geometry
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Initial cluster locations
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Settled cluster locations
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Refined lane geometry
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Intersection refinement
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Intersection refinement
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Intersection refinement
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Refined intersection geometry
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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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Quantitative Evaluation
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Evaluation metrics‣ Geometric evaluation
- Liu et al. (2012)
‣ Topological evaluation- Biagioni & Eriksson (2012)
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Ground truth segment
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Ground truth samples
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Inferred map segment
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Inferred map samples
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Samples compared
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Matching threshold
≤ m? ≤ m? ≤ m? ≤ m?
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m = matching threshold
≤ m? ≤ m? ≤ m? ≤ m?
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≤ m? ≤ m? ≤ m? ≤ m?
Matching threshold
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m = matching threshold
✗ ✗✓ ✓
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≤ m? ≤ m? ≤ m? ≤ m?
“Missing”
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m = matching threshold
✗ ✗✓ ✓
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≤ m? ≤ m? ≤ m? ≤ m?
“Spurious”
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m = matching threshold
✗ ✗✓ ✓
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Overall performance
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precision = 1� |spurious samples||inferred samples|
recall = 1� |missing samples||ground truth samples|
F = 2 · precision · recallprecision+ recall
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Geometric results
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5 10 15 20 25 30
F-sc
ore
Matching Threshold (m)
Biagioni & ErikssonCao
EdelkampDavies
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Topological results
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F-sc
ore
Matching Threshold (m)
Biagioni & ErikssonCao
EdelkampDavies
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Precision/recall
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5 10 15 20 25 30Matching Threshold (m)
F-score Precision Recall
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Qualitative Evaluation
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Edelkamp & Schrödl (2003)
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Edelkamp & Schrödl (2003)
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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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Cao & Krumm (2009)
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Cao & Krumm (2009)
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Biagioni & Eriksson (2012)
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Biagioni & Eriksson (2012)
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Biagioni & Eriksson (2012)
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Future work
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Questions?Thanks!
Source code and data availablehttp://bits.cs.uic.edu