25/11/2013 A method to automatically identify road centerlines from georeferenced smartphone data...
-
Upload
miranda-lloyd -
Category
Documents
-
view
217 -
download
0
Transcript of 25/11/2013 A method to automatically identify road centerlines from georeferenced smartphone data...
25/11/2013
A method to automatically identify road centerlines fromgeoreferenced smartphone data
XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013)
George H. R. Costa, Fabiano Baldo{dcc6ghrc, baldo}@joinville.udesc.br
2
Agenda
Introduction Objective Related work Proposed method Tests and Results Conclusion and Future work
3
Introduction
Digital road maps have gained fundamental role in populationβs daily life Navigation systems etc.
It is essential that maps reflect reality as well as possible Generated from accurate data; Periodic updates.
Possible source of data: GPS traces
4
Introduction
By combining many traces it is possible to generate maps
Example: OpenStreetMap Users use uploaded traces to create/update maps However, all map editing is done manually
Automatic solutions would be more effective Could allow maps to be updated faster Feasible: [BrΓΌntrup et.al. 2005] and [Cao and Krumm
2009] also support this idea
Challenges How to obtain the
data needed to generate maps? Smartphones
Contain many sensors, including a GPS receiver
Represent half of the Brazilian cellphone market [GFK 2013] 5
Source: Garmin
Challenges To create road maps it
is necessary to find the roadsβ centerlines
How to analyze the traces to identify road centerlines? Approximated result
Evolutive algorithm
6
Source: author
7
Objective
Therefore, the objective of this work is to:
Propose a method to identify road centerlines using an evolutive algorithm in orderto generate and update road maps
8
Related work
Characteristics gathered from other works: Independence from initial maps [BrΓΌntrup et.al.
2005; Cao and Krumm 2009; Jang et.al. 2010] Usage of heuristics to remove noise from the traces
[BrΓΌntrup et.al. 2005; Cao and Krumm 2009; Zhang et.al. 2010; Niu et.al. 2011]
Characteristic introduced by this work: Tracesβ date of recording is taken into account to
generate up-to-date maps
Data source
9
Source: author
Preprocessing
Reduces noise; saves all traces to database
10
Source: author
11
Road centerlines
1. Query database to get all traces ordered by date and accuracyi. Most recent firstii. Most accurate first
Source:author
12
Road centerlines
2. For each point k of each trace j ():
1. Identify nearby points All points that intersect a buffer around
Source:author
13
Road centerlines
2. Points with a direction of movement different than are discarded set
3. How to analyze the set to find the road centerline?
Source:author
14
Road centerlines
It is assumed that it is only possible to find an approximated solution
Road centerline = weighted combination between: Date of recording; Accuracy; Distance from a candidate solution to all points
selected ( set).
Chosen algorithm: evolutive algorithm
15
Road centerlines
: candidate solution : set of selected points : influence of time (date of recording) : influence of accuracy : influence of distance
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
16
Road centerlines
, , : Multiply the value of the corresponding influence to prioritize desired characteristics
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
17
Road centerlines
Recent traces: weight closer to 1 Older traces: weight closer to 0
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
πΌπ (ππ)=π‘πππ₯β|π (π π ,ππ )|
π‘πππ₯
Influence of Time
18
Road centerlines
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
πΌπ΄ (ππ )=πΌ π΄ (π π )2+(β1βπΌ βππππ₯
2
ππππ₯) π΄ (ππ )+1
πΌ=βππππβππππ₯ (πππππβ1)ππππ₯ππππ (ππππ₯βππππ)
Influence of Accuracy
Wei
ght
Accuracy
19
Road centerlines
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
πΌπ· (πΆπ₯ ,ππ )=π½π· (πΆπ₯ ,ππ )2+(β1β π½ βππππ₯
2
ππππ₯)π· (πΆπ₯ ,ππ)+1
π½=βππππβππππ₯ (πππππβ1)ππππ₯ππππ (ππππ₯βππππ)
Influence of Distance
Wei
ght
Distance
20
Road centerlines
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
Source:author
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·Closer to highest
concentration of points: smallest overall distance
Closer to points high better accuracy
πΉπΌπππΈππ (πΆπ₯ )=βπ=1
π β² β²
πΌπ (ππ ) βππ +πΌπ΄ (ππ ) βππ΄+πΌπ· (πΆ π₯ ,π π ) βππ·
21
Road centerlines
3. Evolutive algorithm 60 generations 20 candidate solutions per generation Elitism: 2 best candidate solutions are preserved to
the next generation
Source: author
Evolutive algorithm loop:
22
Road centerlines
Evolutive algorithm finds centerline close to Next step: repeat process for If has already been used, skip to the next point
Source:author
23
Results
Implemented in Python DB: PostgreSQL + PostGIS
Data collected between 27/01/2013 e 15/06/2013 4237 traces 966698 points
24
Results
Tests: comparison between Proposed methodβs results Satellite images
Google Earth
Executed on places with complex road structures
25
Tests (1)
Roads intersect
Source: Google Earth / author
Satellite image
26
Tests (1)Points collected (filtered)
Source: Google Earth / author
27
Tests (1)
Way centerline
Direction of movement differentiates traces
It is possible to improve filtering...
Final result
Source: Google Earth / author
28
Tests (2)
Roads with different direction of movement
Roads with same direction of movement
Satellite image
Source: Google Earth / author
29
Tests (2)Points collected (filtered)
Source: Google Earth / author
30
Tests (2)
It is possible to improve filtering...
Direction of movement differentiates traces
Final result
It is possible to improve parameters...
Source: Google Earth / author
31
Results
Small difference between the satellite images and the methodβs results Average distance (100 points): 2.95 meters
Cannot affirm which one is more accurate Certain questions cannot be controlled
Ex.: satellite images might be somewhat out of position
32
Conclusion
Different from similar methods because: Takes into consideration the influence of the tracesβ
date of recording; Collects data using smartphones; Finds centerlines using evolutive algorithm.
Tests showed little difference to satellite images It is still possible to optimize parameters to achieve
better results
33
Future work
Improve collected tracesβ reliability Ex.: Kalman Filter
Different update policies for each region Downtown: more data, only accept better accuracy Rural areas: less data, accept older data
Mining more information Traffic lights Pot holes
34
Bibliografia BrΓΌntrup, R. et. al. (2005) βIncremental map generation with GPS tracesβ. In: Proceedings
of the 8th International IEEE Conference on Intelligent Transportation Systems. Cao, L. e Krumm, J. (2009) βFrom GPS traces to a routable road mapβ. In: Proceedings of
the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, EUA: ACM Press.
Garmin (2010) βGarmin-Asus smartphones reach new marketsβ. <http://garmin.blogs.com/ my_weblog/2010/09/garmin-asus-around-the-globe.html> (accessed on Nov 22).
GFK (2013) βGfK TEMAX BRASIL T2 2013: Crescimento no mercado com forte influΓͺncia de materiais de escritΓ³rio e perifΓ©ricosβ. <http://www.gfk.com/br/news-and-events/press-room/press-releases/Paginas/TEMAX-BRASIL-T2-2013.aspx> (accessed on Nov 18).
Jang, S., Kim, T. e Lee, E. (2010) βMap Generation System with Lightweight GPS Trace Dataβ. In: International Conference on Advanced Communication Technology.
Niu, Z., Li, S. e Pousaeid, N. (2011) βRoad extraction using smart phones GPSβ. In: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications. New York, EUA: ACM Press.
Zhang, L., Thiemann, F., Sester, M. (2010) βIntegration of GPS traces with road mapβ. In: Proceedings of the 2nd International Workshop On Computational Transportation Science. San Jose, EUA. ACM Press.
25/11/2013
A method to automatically identify road centerlines fromgeoreferenced smartphone data
XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013)
George H. R. Costa, Fabiano Baldo{dcc6ghrc, baldo}@joinville.udesc.br