3D Mapping of Pavements: Geometry and DTM
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1 BUDAPEST, HUNGARY D Mapping of Pavements: Geometry and DTM John Laurent, Pavemetrics inc.
2 The Sensor Technology Most Relied-on by DOTs worldwide
3 Collect Your Own, or Contract-out 3
4 Any Paved Surface Hotmix Chipseal Concrete Porous 4
5 APPLICATION: Rails 5
6 APPLICATION: Tunnels 6
7 Application: Airports - FOD
8 Runways currently scanned with Pavemetrics Technology Abu Dhadi (AUH) Bahrain (BAH) Utah (32 airports) Barcelona (BCN) Singapore (SIN) Doha (DOH) Schiphol (AMS) Dubai (DBX) Saint- Louis (STL) Gatwick (LGW) Roma (FCO) Heathrow (LHR) Oman (MCT) Mirabel (YMX) Los Angeles (LAX) 8
9 Application: ROADS distress and DTM
10 Understanding 3D Imaging Range Merged Intensity 10
11 LCMS - Specifictions Excellent 3D Accuracy High Acquisition Rate Sufficient Lateral Resolution LCMS Specifications Acquisition Rate 5,600-11,200 profiles/s Range Accuracy 0.5mm Lateral Resolution 1mm (FOV = 4m) 11
12 LCMS Data Processing Tree IMUs LCMS Data 3D Intensity IRI and Road Geometry Macrotexture and Raveling Cracks and Joints Ruts Pot holes Sealed cracks Lane markings Other road features: Longitudinal cracks Alligator cracks DISTRESS CLASSIFICATION Transversal cracks Manholes Gutters Xings Etc. 12
13 Road Geometry
14 Multiple Coordinate Systems Global UTM (Universal Transverse Mercator) 14
15 Camera/Laser calibration LCMS Sensor Dz Line Laser Camera DZ Range Road/Runway/Tunnel/Rail Surface Successive Scans are Compiled to Form a Continuous Image Dz = k * DZ; Where k is a factor for: distance between surface and lens, focal length of lens, refractive index of lens, distance between lens and sensor, etc
16 IMUs
17 LCMS sensor/imu coordinates transformation IMU
18 Sensor to sensor and world (gravity) position calibration 18
19 Sensor to sensor and world (gravity) position calibration 19
20 Sensor to sensor and world (gravity) position calibration 20
21 Wheel/encoder vs IMU coordinates transformation 21
22 Field Validation Tests
23 Slope/Xslope field validation Slope/Xslope was measured 50 different locations with digital inclinometer (0.1 degree accuracy) Xs were marked on road to identify measurement locations. Xs were identified in LCMS data and Slope/Xslope was determined from simulated reference beam location.
24 Cross-slope: Dynamic vs GT Mean XSlopeErr = 0.07deg Mean XSlopeErr = 0.13 % Std Dev. XSlopeErr = 0.1 deg Std Dev. XSlopeErr = 0.2% Slope (deg.) CrossSlope (deg.) CrossSlopeErr (deg.) (deg.) CrossSlope - - Dynamic - - CrossSlopeGT CrossSlope Measurement Id Measurement Id CrossSlopeErr (deg.) (deg.)
25 Slope: Dynamic vs GT Mean SlopeErr = 0.13deg. Mean SlopeErr = 0.25% Std Dev. SlopeErr = 0.26deg. Slope (deg.) Slope - Dynamic - SlopeGT Slope Measurement Id Std Dev. SlopeErr = 0.46% SlopeErr (deg.) Measurement Id SlopeErr (deg.)
26 Pavemetrics Road tests - Geometry Cross-slope Slope Cross-slope LCMS DEVERS % SENS2 Applanix POS + Point lasers (4) Slope LCMS Applanix POS + Point lasers (4) DECLIVITE % SENS2 26
27 Terrain Mapping
28 Complex Vehicle Dynamics Vehicle acceleration/ deceleration Induite par Pitch/Roll Centripetal force entielle Centripète Gravité Gravity 28
29 Final result Vehicle track and DTM 29
30 3D Road Profile Before Dynamic Corrections
31 3D Road Profile After Dynamic Corrections
32 Field validation 32
33 Field validation Puck center evaluation
34 Field validation Pairing pattern for the 4m test Pairing pattern for the 67m test 34
35 Accuracy tests - RMS error 35
36 Accuracy tests - RMS error y x 36
37 Accuracy tests - RMS error 37
38 LDTM Closing the Loop 3D Road Data Collection New Road Data Processing Road Design Civil engineering 3D road design and infrastructure software Road Condition and Geometry Road Maintenance Priority Project List PMS 38
39 LDTM Closing the Loop 3D Road Data Collection Rehabilitated Road Data Processing LDTM s Data Loop Road Design Civil engineering 3D road design and infrastructure software Road Condition and Geometry Road Maintenance Priority Project List PMS 39
40 Road Profile Before Corrections
41 Road Profile After Geometric and Dynamic Corrections
42 One Sensor; Many Outputs Design Digital Terrain Models Automatic Breaklines Longitudinal Grade Safety Cross Fall Horizontal Curvature Pavement Markings Foreign Object Debris Inventory Linear Referencing GPS Tagging Pavement Type Pavement Length Pavement Width Pavement Images IRI Macro Texture Pavement Management Potholes Raveling Rutting Longitudinal Cracks Transverse Cracks Alligator Cracking Sealed cracks Joint Faulting Transverse Profile 42
43 Any Questions? 43
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