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Characterization of Unpaved Road Conditions through the Use of Remote Sensing Friday, May 2 nd , 2013 – 2 nd Technical Advisory Committee meeting. Colin N. Brooks, Michigan Tech Research Institute (MTRI ) Christopher Roussi , MTRI

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Characterization of Unpaved Road Conditions through the Use of Remote Sensing Friday, May 2nd, 2013 – 2nd Technical Advisory Committee meeting

Colin N. Brooks, Michigan Tech Research Institute (MTRI)

Christopher Roussi, MTRI

Dr. Tim Colling, P.E., Michigan Tech Center for Technology and Training (CTT)

Caesar Singh, P.E., US Department of Transportation (USDOT) Research & Innovative Technology Administration (RITA)

www.mtri.org/unpaved

RITARS-11-H-MTU1

characterization of unpaved road conditions
Characterization of Unpaved Road Conditions

Goal of the Project: Extend available Commercial Remote Sensing & Spatial Information (CRS&SI) tools to enhance & develop an unpaved road assessment system by developing a sensor for, & demonstrating the utility of remote sensing platform(s) for unpaved road assessment.

  • Commercially viable in that it can measure inventory and distress data at a rate and cost competitive with traditional methods
  • Rapid ID & characterization of unpaved roads
  • Inventory level with meaningful metrics
  • Develop a sensor for, & demonstrate the utility of remote sensing platform(s) for unpaved road assessment
  • Platform could be a typical manned fixed-wing aircraft, UAV, or both; depends on relative strengths & weaknesses in meeting user community requirements
  • Simplify mission planning, control of sensor system, & data processing fitting for a commercial entity or large transportation agency
  • Demonstrate prototype system(s) to stakeholders for potential implementation developed through best engineering practices
  • Develop a decision support system to aid the user in asset management and planning
project partners
Project Partners

Partners:

  • Michigan Tech Center for Technology & Training: Gravel roads & Decision Support Tool software expertise
  • Transportation Asset Management Council of Michigan (TAMC) – shared PASER data, provide advice (briefed 1/9/13 on progress, pleased with results
  • SEMCOG (Southeastern Michigan Council of Governments) – shared aerial imagery, provide advice, inventory needs
  • RCOC (Road Commission for Oakland County) – provide advice, local expertise on unpaved roads management needs
  • USDOT-RITA – Program Manager, advice, transportation expertise
  • Michigan Tech Research Institute (MTRI) – project lead, remote sensing, engineering, UAVs, software coding, image processing
assessment method dept army unsurfaced road condition index
Assessment Method: Dept. ArmyUnsurfaced Road Condition Index
  • Representative Sample Segment (approx. . 100’ long)

2 Part Rating System (per distress)

  • Density
    • Percentage of the sample area
  • Severity
    • Low
    • Medium
    • High
  • Drawback: typically takes significant time to

complete manual assessments by traditional

methods

road characteristics
Road Characteristics
  • Unpaved roads have common characteristics
    • Surface type
    • Surface width
      • Collected every 10', with a precision of +/- 4”
    • Cross Section (Loss of Crown)
      • Facilitates drainage, typically 2% - 4% (up to 6%) vertical change, sloping away from the centerline to the edge
      • Measure the profile every 10' along the road direction, able to detect a 1% change across a 9'-wide lane
    • Potholes
      • <1', 1'-2', 2'-3', >3‘ width bins
      • <2”, 2”-4”, >4” depth bins
    • Ruts
      • Detect features >5”, >10' in length, precision +/-2”
    • Corrugations (washboarding)
      • Classify by depth to a precision of +/-1”
        • <1”, 1”-3”, >3”
      • Report total area of the reporting segment affected
    • Roadside Drainage
      • System should be able to measure ditch bottom relative to road surface within +/-2”, if >6”
      • Detect the presence of water, elevation +/-2”, width +/-4”
    • Float aggregate (berms)
inventory surface type
Inventory: Surface Type
  • How many miles of unpaved road are there? Not all counties have this.
  • Need to able to determine this inventory
  • c. 43,000 (1984 estimate) – but no up-to-date, accurate state inventory exists
  • c. 800 miles in Oakland County estimate
  • We are extracting this from recent, high-resolution aerial imagery, focusing on unincorporated areas – attribute existing state Framework roads layer
  • Completed Oakland, Monroe Counties – ready to share with SEMCOG; working on Livingston, St. Clair, Macomb, Washtenaw Counties
slide8

Sensing Unpaved Road Conditions

  • Motivation for Phenomenology Approach: Understand how the physical properties of the road surface distresses manifest themselves in observable ways
    • Color (inc. need for balancing)
    • Texture
    • Patterns
    • profile (inc. 3D structure)
    • Polarization
  • Sensor Nikon D800 – full-sized (FX) sensor, 36.3 Mp, 4 fps - $3,000; 55 mm prime & 105mm lense, 200 mm planned
flight factors for remote control aircraft
Flight factors for remote control aircraft

Forward speed must be low to be able to image with the required scene overlap at the maximum rate of the sensor

Low speed → rotary wing aircraft, since fixed-wing would stall

Must be able to loft 5kg of sensor, controller, and batteries

Must be able to fly for 20min under full load, we’re staying below 100’, in sight of safety pilot;

flight safety effectiveness inspection
Flight Safety & Effectiveness Inspection
  • Evaluate site for safe flight operations, suitable for aerial collection
    • High-voltage towers, restricted airspace, visual obstructions
  • Manned vs. unmanned:
    • Manned: licensed pilot review, FAA regs followed, safety margins included
    • Unmanned: more possible instructions
flight trajectory planning
Flight trajectory planning
  • Ground Station Control program / tool – create flight trajectory
  • Includes ability to automatically take off, fly, auto-land; operator has joystick control at all times
  • Includes Google Earth / Maps information

Typical view of opening screen in Ground Station program

data collection unmanned helicopter
Data Collection – unmanned helicopter
  • Totally autonomous flight.
  • Flight time for a 200 m section: 4 minutes
  • During collects helicopter is flown at 2 m/s and at an altitude of 25 m (82’) and 30 m (98’) – FAA ceiling of 400’

Example flight at http://www.youtube.com/watch?v=KBNQzM7xGQo

other example image
Other Example Image
  • Taken from 25m altitude, 2m/s (1st photo); 30m (2nd )
fixed wing choice
Fixed-wing Choice

FAA restrictions on fixed-wing flight

>500ft altitude

Sensor cannot be attached to aircraft without FAA review

Any small aircraft meet SWAP and flight requirements

While charter costs can be up to $1600-$2500/hr, we flew last fall in a Cessna 172 for $280 for 1.2 hours of flight time

Fly to site, collect data, and fly back

Trial flight 2012; more planned for 2013 after we consulted with President of Professional Aerial Photographers Association, Chuck Boyle

Recommended John Sullivan of AAP Inc. at Ann Arbor Airport

software architecture
Software Architecture
  • Because we are incorporating legacy code, third-party tools, and custom code, we need a flexible architecture
    • Developed in C, C++, Python, bash
    • Flexible control, with tools calling each other as needed
algorithm
Algorithm
  • Use Structure from Motion (SIFT+ Bundler + PVMS) to turn 2D images into 3D point-cloud reconstruction
    • SIFT = scale-invariant feature transform
    • PVMS = patch-based multi-view stereo
  • Form a surface from the 3D point-cloud
    • Form grid, Fourier Filter, Marching Cubes to triangulate
  • Find the depth/height map of the surface
    • Singular Value Decomposition (SVD)
    • Rotate so z-axis is “up” (depth)
algorithm1
Algorithm
  • Find and select the road in the scene
    • Image entropy measure (road is “smoother”)
  • Rotate extracted road into new coordinate system
    • Makes it easier to take cuts along and across road
  • Analyze for features of interest
    • Gabor Filtering, Circular Hough Transform, Cuts for profiles of road and drainage
  • Convert to PASER-like metrics (Pavement Surface Evaluation and Rating System)
  • Generate XML output suitable for RoadSoft GIS decision support processing
example 3d reconstruction
Example 3D Reconstruction
  • 15 images use to form point cloud

Bundler output

Densified point cloud

3D surface from point cloud

Height-field from surface

slide24
3D data examplesImportant to categorizing distresses by severityObtaining 0.9 cm ground sample distance
input to crown measurement
Input to Crown Measurement

Example crossection plot

(vert

meters

Along Road

Across Road

urci density severity deduct
URCI Density, Severity, Deduct

Total Deduct Value = 124

q = # of deduct values = or > 5

q = 4

where next with the project
Where next with the project?
  • Larger set of field deployments along rural roads (Del. 7-A)
    • Both unmanned RC helicopter and Cessna flights
    • Demonstrate hexacopter capability vs. single rotor helicopter
    • Cessna flights shooting at nadir (we have the door that can hold the camera internally)
    • Integrating of results into RoadSoft GIS
  • Write up formal Performance Evaluation (Del. 7-B)
    • How well did we do? How capable is the system? Useful metric generated… Where is technology going towards practical deployment & usage, inc. cost?
  • Avenues for practical usage by transportation agencies
    • in-house model (buy equipment, software)
    • Contracted services model (company performs data collections & analysis for transportation agency; end-to-end system licensed to company)
contact info
Contact Info

Colin Brooks colin.brooks@mtu.edu

Desk: 734-913-6858, Mobile: 734-604-4196

Michigan Tech Research Institute, MTRI

3600 Green Court, Suite 100

Ann Arbor, MI 48105

www.mtri.org

Tim Colling, Ph.D., P.E.tkcollin@mtu.edu

Chris Roussicroussi@mtu.edu

Rick Dobson rjdobson@mtu.edu

David Deandbdean@mtu.edu

DISCLAIMER: The views, opinions, findings and conclusions reflected in this presentation are the responsibility of the authors only and do not represent the official policy or position of the USDOT/RITA, or any State or other entity.