Localization and 3D Reconstruction of Urban Scenes Using GPS · OpenStreetMap (OSM) And...
Transcript of Localization and 3D Reconstruction of Urban Scenes Using GPS · OpenStreetMap (OSM) And...
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Localization and 3D Reconstruction of Urban Scenes Using GPS
Kihwan Kim, Jay Summet, Thad Starner, Daniel Ashbrook,Mrunal Kapade and Irfan Essa
GVU, College of Computing, School of Interactive Computing,Georgia Institute of Technology
{kihwan23,summetj,anjiro,thad,mrunal,irfan}@cc.gatech.edu
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Motivation
3D models in Google earth
• Manual modeling and texturing (sketch-up)• 3D model covers limited area/country• Slow update (aerial picture)
• Nice quality of 3D model • Texture is realistic• Aerial map covers almost every area
+
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Laser scan / Vision based approach for efficient modeling is availableBUT STILL VERY EXPENSIVE
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Motivation
OpenStreetMap (OSM)And Crowd-sourcing
• People share their traces and geographic data from OSM
• Million of data is aggregatedthen can be editable for free
• Cell-phone with GPS enables this kind of crowd-sourcing
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Motivation
1.Can we make an outline of urban scenery with easier and cheaper way?
2. Can we make use of crowd-sourcing concept for easier update and data acquisition ?
Our work started from two questions
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Localize and reconstruct buildings in urban area
By only using off-the-shelf Global Positioning System (GPS)
Goal
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GPS
Overview
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GPS
Overview
We use these signalsTo detect obstacles
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GPS
Overview
Actual signals are reflected based on obstacles.
These multipath reflectors reduce Signal to Noise Ratio(SNR)
Reflected signals
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GPS
Overview
Vectors : GPS to SatelliteEach vector has SNR value
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GPS
Overview
Example:
Gathering SNR on each step.
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GPS
Overview
Then, see what happened in a data chart for recorded SNR on each step
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GPS
Overview
Still having 48~50 dB of SNR
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GPS
Overview
SNR drops to 30 dB
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GPS
Overview
SNR drops to almost 0 dB
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GPS
Overview
Now, SNR is back to 40~50 dB
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Overview
We can infer that something blocks the signals based on change of SNR!
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Overview
Sometimes SNR drops to zero but sometimes less than averages.
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Testing environmentSite 1 Site 2
Top View
Bird’s eye
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Approach (1) Density map generation
Density of chance that buildings or obstacles exist
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Approach (1) Density map generation
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Approach (2) Clustering based on Density
Meanshift clustering to find peaks in Density
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Approach (3) Region map based on each cluster
Region estimation
If we choose this cluster
Using only signals looking at a cluster
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Approach (4) Region estimation
Estimated region (Red) and ground truth region (Yellow)Error from discrepancy is~20% but visually reasonable.
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Approach (5) Make a volume
Our vector data does not give distance/depth information ( no way to find attenuation )Make Voxel where the occluded vector passes over the estimated region.
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Approach (5) Make a volume
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Results
Result Video
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Evaluation
Measurement errors and shape similarities of dominant clusters in two testing environments (BOA, OAC areas)
site CentroidDist(ft)
RegionErr(%)
RealHeight(ft)
EstimatedHeight(ft)
HeightErr(%)
BOA 7.398 22.73 1023 862.80 15.65OAC 22.59 22.20 820 705.32 13.98
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Conclusion
• We have shown that a GPS receiver can detect and localize buildings by measuring reduction of SNR
• Our approach generated reasonable estimates with around 14~22% errors in region and height.
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Conclusion
• Sensor is passive and Inexpensive
the advantages :
• Does not require active aiming
• Automatically self-calibrate
• Using unused signals in conventional GPS system
• Recent cell-phones have GPS capability
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Conclusion
: Millions of shared GPS signals from crowd’s cell phone.
(i.e. Open Street Map)
Crowd-sourcing
- 1 hour to gathering 4000 samples.
What if people gather together?
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Thank you
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Approach
(1) Density map generation
See the paper for more details!
Density map
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