Figure 1: Mobile mapping project location in New York City.

Transcription

1 Using Mobile LiDAR to Deliver Survey Accurate Data This presentation will take the attendees though the process of controlling and delivering survey grade LiDAR data for use in CADD, Modeling, and GIS applications. We will first provide the information needed to define and layout adequate survey control. Next we will layout the processes used to calibrate and register LiDAR point clouds to survey control. Then we will finalize the presentation discussing the methodologies and tools utilized to create survey grade GIS and CADD deliverables and discuss those deliverables and how they apply to the Design world. The presentation will include real world examples and cases studies highlighting different acquisition scenarios. Collecting mobile LiDAR for use in survey grade projects has to begin with the acquisition planning process. When a project is identified that mandates the absolute accuracies required for survey grade deliverables the mission planning of control point numbers and locations are crucial. The first step in this process is to define the end deliverables and the project accuracy requirements. Deliverables could be 2D CADD plans, 3D CADD files with surfaces, LiDAR Point Cloud for use in BIM/Mesh modeling, or GIS databases for highway operations, maintenance, or design. Figure 1: Mobile mapping project location in New York City. Once the deliverables have been defined the acquisition planning begins. The acquisition planning process includes the project location analysis, need for local GPS base or use of a VRS/CORS station, the actual planning of the collection mission trajectories, the need for multiple passes or single pass of collection, and finally the location of control and validation points.

2 Figure 2: Mobile mapping collection trajectories and control/validation locations. The location and numbers of survey control points required for LiDAR registration varies between different projects and types of collection locations. In urban locations more control is needed because the collection trajectories are smaller and follow the grid pattern of the street layouts. Control should be placed at intersections where multiple passes overlap. Also control points placed at mid points within each block are warranted. In rural or highway collection, survey control is needed, but not as densely. Travel lanes and intersections are still the best locations. Control points can be established via RTK or traditional survey (closed loop traverse) methods. The method used is based upon project deliverables and location. They types of control markers range based upon systems, however 2 chevrons that are 6 wide work well. A PK nail at the tip is preferred. In urban locations, photo id locations can be used. Those range from corners of inlets, center of valves, edges of ADA ramps, points of turn arrows, corners of stop bars, etc. Any fixed point that can be located within the LiDAR point cloud.

3 Figure 3: Survey control targets (chevrons with PK nails at the tip) as seen in the LiDAR point cloud and the associated imagery. Upon the field acquisition completion, the post processing begins. Within this part of the overall project the inclusion of the survey control takes place in the creation of the LiDAR point clouds. The first step is the creation of the mobile mapping trajectory that is used to build the LiDAR point clouds. The more accurate this trajectory is the more accurate the developed point clouds will be. Within the tools to build the solution, the use of the local GPS base stations or the CORS/VRS outputs are used to post process each 1 second epoch of the mobile mapping s onboard GNSS/IMU output. For survey accurate project, the actual survey control points can also be used to publish a highly accurate trajectory.

4 Figure 4: Waypoint Inertial Explorer used to publish the mobile mapping trajectory. Depending upon the collection methodology of a single pass versus multiple pass there may be more than one LiDAR point cloud trajectory created for each physical location within the project limits. Each of these separate trajectories and subsequent LiDAR point clouds will overlap each other and will have to be aligned. Figure 5: Multiple trajectories and LiDAR Point Clouds in the same location.

5 These multiple trajectories must be aligned prior to injection any survey control to the data. This 3D alignment is accomplished at the trajectory level and then new LiDAR point clouds are created. To do this the use of a technique called photo-id tagging is leveraged. Each of the individual LiDAR point clouds are reviewed for common physical elements such as corners of inlets, corners of painted stop bars, etc. Pseudo control points are then created in each overlapping point cloud at these locations then the trajectories are reprocessed using these photo-id points as ties. New LiDAR point clouds are then created. Figure 6: Two point clouds overlapped and photo-id points created in each for trajectory reprocessing. Figure 7: Three LiDAR point clouds aligned after initial 3D alignment processing. Now that the multiple trajectories are aligned, the registration of the trajectory to the survey control can now happen. This will move all the mobile mapping trajectories to the accuracy of the control. The same process of photo-id is used for the aligned trajectories to the survey control. The difference in this step is that the pseudo points are only needed to be placed within the individual LiDAR point clouds. They are added to the point cloud where the actual survey control points are. The same trajectory alignment reprocessing as used in the 3D alignment process is now used with the trajectory and the survey control point, however now the control point features are locked and the trajectory is moved to the actual survey locations.

6 Figure 8: Three LiDAR point clouds aligned after initial 3D alignment processing. After this process is completed the LiDAR point clouds now have the absolute accuracy needed for creation of the deliverables. Figure 9: Registered LiDAR Point Cloud to Survey Control.

7 The last task in any survey grade mobile mapping project is the creation of the final deliverables. 90% of the time a survey grade project will require the extraction of features from the LiDAR point clouds. Prior to beginning the feature extraction process, most clients will require LiDAR point accuracy reports. These use of the validation points (blue points shown in Figure 2 above) placed from the original survey control placement are now used to check the horizontal (x,y) and the vertical (z) deltas and RMSE values from the point cloud to the validation points. Figure 10: Sample LiDAR Point Cloud absolute accuracy report. After the client has accepted the LiDAR point cloud s accuracies the feature extraction will begin. There are many software tools in the market that allow for feature extraction from a LiDAR point cloud. Some use a virtual surveyor process; some use the intensity of the LiDAR points and lastly, some use a combination of the imagery collected and the LiDAR points. Regardless of the tool(s) of choice, points, lines, and polygons are extracted from the LiDAR data to be used in a CADD, GIS, or Modeling environment.

8 Figure 11: Leica s Cyclone using virtual surveyor tool to code survey points from the LiDAR data. Once all the feature extraction has been completed, the final step is started. This is the development of actual deliverables that most clients will use. These range from 2D and 3D CADD files, DTM surfaces, Revit Models, and GIS geodatabases. The following figures show examples of these end products. Figure 12: AutoDesk s AutoCAD Civil 3D plan drawing created from the extracted features.

9 Figure 13: Microstation Inroads used to create 3D models. Figure 14: Microstation Inroads used to create TIN and Smooth Surfaces.

10 Figure 15: Microstation detailed 3D drawings of ADA curb ramp from LiDAR point cloud. Figure 16: Esri s ArcGIS with a geodatabase containing survey accurate data.