1 / 29

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

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

orrick
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. Project web pagehttp://www.mtri.org/unpaved • http://www.mtri.org/unpaved/

  4. 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

  5. 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

  6. 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)

  7. 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

  8. 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

  9. 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;

  10. Selected initial aircraft: Bergen Tazer 800

  11. 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

  12. 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

  13. 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

  14. Piotter Rd. and Garno CollectNovember 8, 2012

  15. Helicopter Data – Piotter Rd.25 m Altitude

  16. Other Example Image • Taken from 25m altitude, 2m/s (1st photo); 30m (2nd )

  17. Ground data being collected for all roads being flown for assessment

  18. 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

  19. Aerial Collect

  20. 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

  21. 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)

  22. 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

  23. Example 3D Reconstruction • 15 images use to form point cloud Bundler output Densified point cloud 3D surface from point cloud Height-field from surface

  24. 3D data examplesImportant to categorizing distresses by severityObtaining 0.9 cm ground sample distance

  25. Input to Crown Measurement Example crossection plot (vert meters Along Road Across Road

  26. URCI Density, Severity, Deduct Total Deduct Value = 124 q = # of deduct values = or > 5 q = 4

  27. URCI Assigned Distress ID & Ranking in the RoadSoft GIS DSS

  28. 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)

  29. 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.

More Related