Friday, October 1, 2010

Modeling Bridges from Mobile LiDAR Information

I often tell people that the products derived from Mobile LiDAR data are only limited by one's imagination and willingness to push the envelope. On that premise, we recently performed work on an interstate including overpasses and ramps.  In addition to the typical MicroStation planimetrics and DTM, Baker's Applied Technology (AT) group was enlisted to prepare models of collected bridges. While looking at the progression of the modeling, keep in mind that the information was collected at posted speed limits.

Below is a simple slice of the Mobile LIDAR point cloud representing the North/South bound lanes of travel. I quickly added height clearances which I used to calculate an approximate slope. At each of the bridges, we created subsets of the point cloud to minimize processing during the modeling phase.

From the subset point cloud, our AT staff began modeling the environment.  Presented below is a wire-frame of the modeled solids (modeling involves developing a mathematical representation of 3-dimensional features).  The ground is represented as a Triangulated Irregular Network (TIN).  In a wire-frame, the outlines of the features are depicted with the point cloud still visible.

After the wire-frame, a hidden line model is presented below.  Basically the shapes and surfaces shown in the wire-frame are filled.  The solids depicted have no texture, but the model begins to take shape.

Then a draft of the model is prepared for visual inspection.  The impact of grass in the median is clearly visible.  Since we started without the luxury of a classified point cloud, our AT staff cleaned the surface using MicroStation InRoads (can also be completed in GeoPak).  Obviously starting with a classified LAS containing bare-earth and vegetation points, would have saved a little time for our modelers.

Perhaps the coolest image, in my opinion, is the Ambient Occlusion (AO)image shown below.  The software we utilize provides the ability to create shadows, define sun angles and change perspective depending on time of day.  I also like that it shows an immense amount of detail on the guardrails and other features. 

By adding vehicles to the model, it provides a sense of scale and depth. The vehicles in the AO image are used to determine the shadows they cast on the model as well.

Finally, textures and colors are applied to modeled features.  The shadows cast are incredibly detailed - notice the guardrail and pillars on the right-hand side.  The next step is to add cars, collected roadway signs and other ancillary information - perhaps our team can throw a model of the Mobile LiDAR unit in there rolling down the interstate.

For more information on Baker's Applied Technology and the services they provide, please contact:

Senior Designer
3D Design and Visualization
Michael Baker Jr. Inc.

Alan and the rest of the group have exceeded our expectations time and time again.  Perhaps down the road I'll share more of their superior work modeling Mobile LiDAR data.

1 comment:

  1. Very cool. My limited experience in taking laser scanner data and converting it to a finished model left me wondering how people got such great visual results from such dense (and sometimes confusing) data.
    This really helps to see the visual overview of the full process from data to finished model.