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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‣ 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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F-sc

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Matching Threshold (m)

Biagioni & ErikssonCao

EdelkampDavies

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Topological results

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F-sc

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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