jay a. farrell, university of california-riverside james a ... · jay a. farrell, university of...
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Precision Roadway Feature Mapping Jay A. Farrell, University of California-Riverside James A. Arnold, Department of Transportation
February 26, 2013
ESRA Fed. GIS
Outline: • Big picture: Positioning and Mapping • Precision Mapping • Real-time Vehicle Position Estimation • Project: Results and Applications • Interesting Directions
GPS/IMU
LIDAR
panoramic camera
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Precision Automated Mapping & Positioning • Automated Highway Inventory mapping and
characterization enables: – More efficient management of highway assets – Improved availability of lane-level positioning accuracy
• Positioning with high degrees of:
– Accuracy – Less than one meter – Continuity – High sample rate – Availability – Works in diverse environments – Accomplished at Low Cost
Enables ITS applications with transformative improvements for safety, mobility, and environmental performance
ITS Safety & Mobility Applications: Lane-departure warning, Freeway merge assistance, Curve over-speed warning, Adaptive cruise control, Congestion warning systems, Signal Phase and Timing, Lane-level Intersection management and collision avoidance, Headlight steering into curves
Automated, sensor-based mapping for accurate and low-cost roadway map production for “where-in-lane” accuracy
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Raw Data
Smoothing/ Feature
Extraction
Database
LidarVision GPS/INS
Database Management
Tool
Database
Mapping Data Accumulation
Mapping DataBaseDevelopment
Application Development
Application Software:
Navigation + Feature
Detection
Database
Vision GPS/INS
hing/ ure ction
Feature Data
Offline Processing
Export
Precision Mapping & Real-time Positioning
Sensor???
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Raw Data
Smoothing/ Feature
Extraction
Database
LidarVision GPS/INS
Mapping Data Accumulation
hing/ urection
Feature Data
Offline Processing
sensor platform
Precision Roadway Map: Data Acquisition GPS IMU
Panoramic Camera
LIDAR
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5
Mapping Process
Equipment: • LIDAR • Camera set • IMU • GNSS Receiver • High capacity HD • Roof Platform • Power supply • Matlab • DGPS station • Data Communication • GIS • High Performance CPU
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Driving and data collection …
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IMU and GPS data are smoothed providing a continuous sensor platform trajectory
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Trajectory Estimation • Solve for Integer Ambiguity
– Smooth trajectory using inertial measurements, pseudo-range, and Doppler measurements
– Use smooth trajectory to find integer intervals
– Extend integers from known intervals to adjacent intervals
• Final Trajectory Smoothing – Use integers when available,
otherwise, use pseudo-range and Doppler
• Can be Utilized Extensively in Mapping
• See: A. Vu et al., (2012) “Improved Vehicle Trajectory and State Estimation using Raw GPS and IMU
Data Smoothing,” IEEE Intelligent Vehicle Symposium, Madrid Spain, June 2012.
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Trajectory Estimation Cont.
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Features (e.g., road signs, lane markings, road curves, etc.) are extracted and identified from the raw data
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Traffic Sign Extraction…
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Plane Feature Estimation see corresponding sign numbers and normals
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Estimated Plane Position vs. Surveyed Plane Position
Sign Post #
Survey North (m)
Survey East (m)
Max Survey ECEF Std (m)
Computed North (m)
Computed East (m)
Abs. Horiz. Error (m)
1 -41.192 -87.313 0.042 -41.133 -87.345 0.0672 -55.321 -62.332 0.126 -55.271 -62.328 0.0503 -89.666 -36.001 0.159 -89.656 -36.112 0.1114 -142.194 80.051 0.057 -142.244 80.054 0.0505 -139.078 146.342 0.047 -139.101 146.358 0.0286 -115.571 144.863 0.009 -115.658 144.923 0.1067 -85.860 141.540 0.031 -85.882 141.543 0.0228 -47.903 122.360 0.025 -47.952 122.415 0.0739 -35.369 99.666 0.045 -35.381 99.686 0.023
10 -44.498 101.259 0.024 -44.433 101.281 0.06811 -62.597 125.258 0.04 -62.555 125.306 0.06312 -134.138 76.493 0.025 -134.073 76.519 0.06913 -104.598 -4.185 0.061 -104.569 -4.152 0.04416 -118.704 133.631 0.015 -118.618 133.724 0.126
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Sign Identification Process…
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Feature Extraction and Identification (2) • Curve Extraction
– Primary sensor: LIDAR 64-laser, 20 Hz laser range finder – Processing step 1:
• Ground plane image projection from measurements • Output: intensity as a function of position
– Processing step 2:
• Curve detection using projected vehicle trajectory on ground plane images • Output: Curves composed of a set of sequential 3D positions in world frame
• Curve Parameterization – Primary input: curves from previous step – Processing step: Curve Fitting – Output: roadway curve format suitable for GIS
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LIDAR Intensity Point Cloud…
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Bing Map with TFHRC aerial image overlay
Map Representation in ArcGIS
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Georectified LIDAR-based intensity image
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Bing Map, TFHRC Aerial Image, LIDAR-based intensity image overlay
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Finding Lanes and Curves
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Curve Fitting
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Map Database
Raw Data
Smoothing/ Feature
Extraction
Database
LidarVision GPS/INS
Database Management
Tool
Database
Mapping Data Accumulation
Mapping DataBaseDevelopment
Application Development
Application Software:
Navigation + Feature
Detection
Database
Vision GPS/INS
hing/ ure ction
Feature Data
Offline Processing
Export
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Applications
Raw Data
Smoothing/ Feature
Extraction
Database
LidarVision GPS/INS
Database Management
Tool
Database
Mapping Data Accumulation
Mapping DataBaseDevelopment
Application Development
Application Software:
Navigation + Feature
Detection
Database
Vision GPS/INS
hing/ ure ction
Feature Data
Offline Processing
Export
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inexpensive GPS/IMU
No LIDAR
inexpensive rectilinear camera
sensor platform
Sensor platform for positioning
No panoramic camera
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Application Data Flow
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Positioning: Aided Navigation
High rate sensors: provide high bandwidth, continuously available vehicle state
• Candidate sensors: Encoder or IMU
Aiding measurements: maintain vehicle state accuracy
• Candidate sensors: GPS, Camera, Lidar, Radar, DSRC, Signals-of-Opportunity
See J.A. Farrell, Aided Navigation, McGraw-Hill, 2008.
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Map Representation in Positioning Graphical User Interface (GUI)
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Application Graphical User Interface…
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Lane Departure Warning…
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Curve Overspeed Warning…
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Aiding Sensor Categories
GNSS: Proven with open skies. Challenging in urban environments. TRN: Shows great promise as physical layer timing advances FB: Assessed as viable if precision roadway feature maps are available. Research leading to
reliable and accurate FB positioning demonstrations was a project focus.
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Sign Aiding …
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Project Summary
• Automated sensor-based mapping is necessary for nationwide lane-level map production – This project developed software and demonstrated automate
sensor-based mapping is feasible with centimeter-level accuracy
• Three lane-level applications built on the foundation of lane-level maps were demonstrated using decimeter-level positioning techniques – Lane departure warning – Curve overspeed warning – Signal Phase and Timing, at lane-level
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Some Ideas for Future Work • Positioning
– DSRC modem based time-of-flight positioning • Direct contacts with manufacturers are in place
– Terrestrial RF Signals of Opportunity: DTV, Digital radio and cellular – Enhance performance and robustness of feature based methods,
especially RADAR and LIDAR – Investigate safety & integrity monitoring for data fused vehicle applications
• Mapping – Improved and more fully automated mapping processes – Mapping additional feature types – Thorough evaluation in less-structured, more-dynamic environments – Maintenance of the precision map
• Crowd sourcing • Targeted updates
– Large scale computer or cloud implementation to map larger environments • Positioning and Mapping
– Thorough evaluation in less-structured, more-dynamic environments