LiDAR processing for road network asset inventory

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LiDAR processing for road network asset inventory

  1. 1. LiDAR processing for road network asset inventory<br />Conor Mc Elhinney, Tim McCarthy<br />Tuesday 28th September 2010<br />
  2. 2. Outline<br /><ul><li> LiDAR processing workflow
  3. 3. EuRSI
  4. 4. Road Edge Extraction
  5. 5. Pole Detection and Extraction
  6. 6. Resolution Effect of speed on scan lines
  7. 7. Active Contours
  8. 8. Publications
  9. 9. Conclusion</li></li></ul><li>Outline<br /><ul><li> LiDAR processing workflow
  10. 10. EuRSI
  11. 11. Road Edge Extraction
  12. 12. Pole Detection and Extraction
  13. 13. Resolution Effect of speed on scan lines
  14. 14. Active Contours
  15. 15. Publications
  16. 16. Conclusion</li></li></ul><li>Geo-referencing the data<br />Workflow<br />GPS<br />Base Station<br />GPS<br />Mobile<br />
  17. 17. Geo-referencing the data<br />Workflow<br />GPS<br />Base Station<br />GPS<br />GPS<br />Mobile<br />
  18. 18. Geo-referencing the data<br />Workflow<br />INS<br />GPS<br />Base Station<br />NAV<br />GPS<br />GPS<br />Mobile<br />
  19. 19. Geo-referencing the data<br />Workflow<br />INS<br />LiDAR<br />GPS<br />Base Station<br />NAV<br />+<br />GPS<br />Geo-referenced Point Cloud<br />GPS<br />Mobile<br />
  20. 20. Geo-referencing the data<br />Workflow<br />INS<br />LiDAR<br />Imagery<br />GPS<br />Base Station<br />NAV<br />+<br />+<br />GPS<br />Geo-referenced Point Cloud<br />Geo-referenced Imagery<br />GPS<br />Mobile<br />
  21. 21. Geo-referencing the data<br />Workflow<br />INS<br />LiDAR<br />Imagery<br />GPS<br />Base Station<br />NAV<br />+<br />+<br />GPS<br />Geo-referenced Point Cloud<br />Geo-referenced Imagery<br />GPS<br />Mobile<br />+<br />Geo-referenced Multi-Source Data<br />
  22. 22. NRA Fellowship Application<br />The central aim of this project is to design, construct and validate a comprehensive and innovative, road-network asset inventory software application<br />Segments<br />l x w x h<br />LiDAR<br />(GBs)<br />Process<br />Knowledge<br />Road, curvature...<br />Sign posts, lamps..<br />Bridges, fences...<br />
  23. 23. NRA Fellowship Application<br />The central aim of this project is to design, construct and validate a comprehensive and innovative, road-network asset inventory software application<br />that enables LiDAR and LiDAR/image data to be automatically and semi-automatically processed<br />LiDAR<br />(GBs)<br />Segments<br />l x w x h<br />Sign Processing<br />Road Processing<br />Knowledge<br />Road, curvature...<br />Sign posts, lamps..<br />?<br />
  24. 24. File Preparation workflow<br />Block 1<br />Geo-referenced Multi-Source Data 1<br />Block 2<br />Geo-referenced Multi-Source Data 2<br />Split / Merge / Join into Geographic Blocks<br />Block 3<br />Geo-referenced Multi-Source Data 3<br />.<br />.<br />.<br />.<br />.<br />.<br />Geo-referenced Multi-Source Data N<br />Block N<br />
  25. 25. File Preparation workflow<br />Block 1<br />Processing<br />Knowledge<br />Block 2<br />+<br />Block 3<br />.<br />.<br />.<br />Block N<br />+<br />
  26. 26. What are you left with?<br />LiDAR folder<br />Block 1<br />Block 1<br />Block 1<br />Block 2<br />Block 2<br />Block 2<br />Survey 10 Apr<br />Survey 5 Dec<br />Survey 2 May<br />Block 3<br />Block 3<br />Block 3<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.<br />.......<br />Block N<br />Block N<br />Block N<br />MetaData: Geo Bounds, date, processing done<br />MetaData: Geo Bounds, date, processing done<br />MetaData: Geo Bounds, date, processing done<br />
  27. 27. Question<br />Give me all the lidar data in dublin 4?<br />LiDAR folder<br />
  28. 28. Question<br />Give me all the lidar data in dublin 4 between December 2009 and 2010?<br />LiDAR folder<br />
  29. 29. Question<br />Give me only the aerial DEMs created in Dublin 4?<br />LiDAR folder<br />
  30. 30. Question<br />New aerial MMS data has been collected, select the relevant terrestrial MMS data and refine the results from previous algorithms.<br />LiDAR folder<br />
  31. 31. Workflow solutions<br />This is why there are company’s who specialise in developing workflow solutions.<br />
  32. 32. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />
  33. 33. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />GIS <br />DB<br />GIS <br />DB<br />
  34. 34. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />Spatial Query<br />GIS <br />DB<br />GIS <br />DB<br />
  35. 35. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />Spatial Query<br />GIS <br />DB<br />GIS <br />DB<br />Cam 1<br />Cam 2<br />Point Cloud<br />
  36. 36. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />Spatial Query<br />GIS <br />DB<br />GIS <br />DB<br />Cam 1<br />Cam 2<br />Point Cloud<br />}<br />Data Fusion / Processing<br />
  37. 37. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />Spatial Query<br />GIS <br />DB<br />GIS <br />DB<br />Cam 1<br />Cam 2<br />Point Cloud<br />}<br />Road Data Info<br />Data Fusion / Processing<br />
  38. 38. Our Solution<br />LIDAR data<br />Imagery<br />.......<br />Spatial Query<br />GIS <br />DB<br />GIS <br />DB<br />Cam 1<br />Cam 2<br />Point Cloud<br />}<br />Road Data Info<br />Data Fusion / Processing<br />Visualisation <br />
  39. 39. Video – Web spatial query<br />
  40. 40. Demo – Desktop navigation<br />
  41. 41. Demo – 2D query<br />
  42. 42. Demo – 3D query<br />Load 3D data for pole extraction<br />
  43. 43. Outline<br /><ul><li> LiDAR processing workflow
  44. 44. EuRSI
  45. 45. Road Edge Extraction
  46. 46. Pole Detection and Extraction
  47. 47. Resolution Effect of speed on scan lines
  48. 48. Active Contours
  49. 49. Publications
  50. 50. Conclusion</li></li></ul><li>What are we trying to do?<br />n<br /><ul><li> We aim to develop a automated approach to the extraction of the road surface from the data from land based Mobile Mapping Systems (MMS).</li></li></ul><li>What are we trying to do?<br />n<br /><ul><li> We aim to develop a automated approach to the extraction of the road surface from the data from land based Mobile Mapping Systems (MMS).</li></ul>LiDAR point cloud<br />+<br />Imagery<br />
  51. 51. What are we trying to do?<br />n<br /><ul><li> We aim to develop a automated approach to the extraction of the road surface from the data from land based Mobile Mapping Systems (MMS).</li></ul>LiDAR point cloud<br />Processing<br />+<br />+<br />Imagery<br />
  52. 52. What are we trying to do?<br />n<br /><ul><li> We aim to develop a automated approach to the extraction of the road surface from the data from land based Mobile Mapping Systems (MMS).</li></ul>Road Surface<br />LiDAR point cloud<br />Processing<br />+<br />+<br />Imagery<br />
  53. 53. What are we trying to do?<br />n<br /><ul><li> We aim to develop a automated approach to the extraction of the road surface from the data from land based Mobile Mapping Systems (MMS).
  54. 54. Once we have converted the point cloud data into a surface, we can start extracting the geometrical properties of the road.</li></ul>Road Surface<br />Road Geometry<br />LiDAR point cloud<br />Processing<br />Centreline / Width ...<br />+<br />+<br />Crossfall<br />Grade / Curvature<br />Imagery<br />
  55. 55. What are we trying to do?<br />semi-<br />n<br /><ul><li> We aim to develop a automated approach to the extraction of the road surface from the data from land based Mobile Mapping Systems (MMS).
  56. 56. Once we have converted the point cloud data into a surface, we can start extracting the geometrical properties of the road.</li></ul>Road Surface<br />Road Geometry<br />Processing<br />LiDAR point cloud<br />Centreline / Width ...<br />+<br />+<br />+<br />Crossfall<br />Grade / Curvature<br />Imagery<br />
  57. 57. What have we achieved so far?<br /><ul><li> We have developed the an algorithm for finding the road edges which are the primary input into our future road surface extraction algorithm.</li></ul>Road Surface<br />Processing<br />LiDAR point cloud<br />+<br />+<br />+<br />Imagery<br />
  58. 58. What have we achieved so far?<br /><ul><li> We have developed the an algorithm for finding the road edges which are the primary input into our future road surface extraction algorithm.
  59. 59. It takes as input only the LiDAR point cloud.</li></ul>Road Surface<br />Processing<br />LiDAR point cloud<br />+<br />+<br />+<br />Imagery<br />
  60. 60. What have we achieved so far?<br /><ul><li> We have developed the an algorithm for finding the road edges which are the primary input into our future road surface extraction algorithm.
  61. 61. It takes as input only the LiDAR point cloud.
  62. 62. There is no manual stages to date.</li></ul>Road Surface<br />Processing<br />LiDAR point cloud<br />+<br />+<br />+<br />Imagery<br />
  63. 63. What have we achieved so far?<br /><ul><li> We have developed the an algorithm for finding the road edges which are the primary input into our future road surface extraction algorithm.
  64. 64. It takes as input only the LiDAR point cloud.
  65. 65. There is no manual stages to date.
  66. 66. We can extract the left and right edges without bias to the road type</li></ul>Processing<br />Road Edges<br />LiDAR point cloud<br />+<br />+<br />+<br />Imagery<br />
  67. 67. What have we achieved so far?<br /><ul><li> We have developed the an algorithm for finding the road edges which are the primary input into our future road surface extraction algorithm.
  68. 68. It takes as input only the LiDAR point cloud.
  69. 69. There is no manual stages to date.
  70. 70. We can extract the left and right edges without bias to the road type
  71. 71. We will then use this information to extract the road points and calculate its surface.</li></ul>Road Surface<br />Processing<br />Road Edges<br />LiDAR point cloud<br />+<br />+<br />+<br />Imagery<br />
  72. 72. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.</li></li></ul><li>The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  73. 73. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  74. 74. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  75. 75. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  76. 76. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  77. 77. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  78. 78. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  79. 79. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  80. 80. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  81. 81. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  82. 82. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  83. 83. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  84. 84. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  85. 85. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  86. 86. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  87. 87. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br />
  88. 88. The Big Picture<br /><ul><li> We’re going to drive four regional roads of 100km in four countries selected by the national roads authorities of these countries.
  89. 89. Semi-automatic feature extraction</li></ul> - road edges<br /> - road surface<br /> - road geometry<br /> - roadside signs / poles / trees<br /> - roadside vegetation<br /> - roadside features, crash barriers<br /><ul><li> Feed constrained data into a machine learning algorithm which will output a risk assessment matrix.</li></li></ul><li>Outline<br /><ul><li> LiDAR processing workflow
  90. 90. EuRSI
  91. 91. Road Edge Extraction
  92. 92. Pole Detection and Extraction
  93. 93. Resolution Effect of speed on scan lines
  94. 94. Active Contours
  95. 95. Publications
  96. 96. Conclusion</li></li></ul><li>Size<br />Speed: 55 km/h<br />Left: 1000 pts/m2<br />Right: 175 pts/m2<br />61m<br />6m<br />
  97. 97. Right Edge - Curbstone<br />
  98. 98. Right Edge - Curbstone<br />
  99. 99. Left Edge - Embankment<br />
  100. 100. Left Edge - Embankment<br />
  101. 101. Car Occluding Road Edge<br />
  102. 102. Car Occluding Road Edge<br />
  103. 103. Road Edge Extraction<br />Direction <br />of travel<br />
  104. 104. Road Edge Extraction<br />
  105. 105. Road Edge Extraction<br />
  106. 106. Road Edge Extraction<br />
  107. 107. Road Edge Extraction<br />
  108. 108. Road Edge Extraction<br />
  109. 109. Road Edge Extraction<br />
  110. 110. Road Edge Extraction<br />
  111. 111. Road Edge Extraction<br />
  112. 112. Road Edge Extraction<br />
  113. 113. Road Edge Extraction<br />
  114. 114. Road Edge Extraction<br />
  115. 115. Demo – Road Edges<br />
  116. 116. Demo – Road Edges<br />
  117. 117. Outline<br /><ul><li> LiDAR processing workflow
  118. 118. EuRSI
  119. 119. Road Edge Extraction
  120. 120. Pole Detection and Extraction
  121. 121. Resolution Effect of speed on scan lines
  122. 122. Active Contours
  123. 123. Publications
  124. 124. Conclusion</li></li></ul><li>Demo – Pole Extraction<br />
  125. 125. (ITC)<br />Pole Detection, Classification<br /><ul><li> Different workflow to road extraction
  126. 126. Region Growing – Co-Planar Points
  127. 127. Identify Ground, Wall, Pole-like structures
  128. 128. Separate Pole-like Objects</li></li></ul><li>Results (EuroSDR)<br />
  129. 129. Classification by Height<br />
  130. 130. Results<br />
  131. 131. Demo – Pole Extraction<br />
  132. 132.
  133. 133.
  134. 134.
  135. 135. EuRSI: <br />After<br /><ul><li> We are still working on EuRSI, it is our aim to finish and test the current road extraction and pole extraction processing tools in the coming months.
  136. 136. Once these are completed we would intend to meet with our NRA colleagues to demonstrate these tools and to discuss the alternative feature extraction process’ with the purpose of using the expertise of the NRA to help direct our application development.</li></li></ul><li>Outline<br /><ul><li> LiDAR processing workflow
  137. 137. EuRSI
  138. 138. Road Edge Extraction
  139. 139. Pole Detection and Extraction
  140. 140. Resolution Effect of speed on scan lines
  141. 141. Active Contours
  142. 142. Publications
  143. 143. Conclusion</li></li></ul><li>Vertical offset<br />
  144. 144. Horizontal offset<br />
  145. 145. Measured values<br />
  146. 146. Results<br />
  147. 147. Outline<br /><ul><li> LiDAR processing workflow
  148. 148. EuRSI
  149. 149. Road Edge Extraction
  150. 150. Pole Detection and Extraction
  151. 151. Resolution Effect of speed on scan lines
  152. 152. Active Contours
  153. 153. Publications
  154. 154. Conclusion</li></li></ul><li>Active Contours<br />Active contours are a 2D image processing technique.<br />We are attempting to bring them into LiDAR as tools to find homogenous regions in elevation, amplitude, pulse width return....<br />
  155. 155. Outline<br /><ul><li> LiDAR processing workflow
  156. 156. EuRSI
  157. 157. Road Edge Extraction
  158. 158. Pole Detection and Extraction
  159. 159. Resolution Effect of speed on scan lines
  160. 160. Active Contours
  161. 161. Publications
  162. 162. Conclusion</li></li></ul><li>Outline<br /><ul><li> LiDAR processing workflow
  163. 163. EuRSI
  164. 164. Road Edge Extraction
  165. 165. Pole Detection and Extraction
  166. 166. Resolution Effect of speed on scan lines
  167. 167. Active Contours
  168. 168. Publications
  169. 169. Conclusion</li></li></ul><li>Conclusion<br />That’s a summary of the work we’ve completed in the last 10 months. <br />Aiming to build a framework for LiDAR processing that encompasses data storage/access/management, accuracy analysis, resolution prediction, automatic processing and knowledge extraction.<br />This framework will allow for all the different data sources to be incorporated at any stage and the data to be easily accessed at any time.<br />Geo-3D, <br />http://www.geo-3d.com<br />Creeman et at., <br />IEEE Robotics and Automation, 2006<br />

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