Efficient Measurement of Bridge and Overhead Structure Clearance Information at Posted Highway Speeds

For most DOTs, knowledge of vertical clearances between the paved roadway surface and vertical structures is an important piece of information that supports the routing of oversized permit vehicles. In addition, horizontal clearances under overhead structures between fixed objects such as bridge columns, railings and median barriers are also important to ensure oversized objects do not impact the structure. Most DOTs use this information for posting clearance signs identifying the vertical clearance of structures and utilize the horizontal clearance information to route oversized vehicles. There are also Federal reporting requirements as part of the National Bridge Inventory (NBI) program that is administrated by FHWA. Many DOTs measure vertical clearances as a single, minimum value under each bridge or overhead structure. This is typically measured by field personnel who are exposed to moving traffic, lane closures and traffic delays, which create safety issues along the road. This manual measurement methodology can also be inaccurate because of the “human factor” involved in making these measurements. The position of the minimum value gets applied to the entire structure, even though it may be in a position that can easily be avoided with proper planning. This methodology can also create situations where a manual measurement methodology may not identify the true minimum clearance because it was missed because of the measurement technology limitations. There are a handful of DOTs in the industry who are using mobile LiDAR technology to inventory their overhead obstructions using mobile LiDAR and right-of-way imagery. This blend of technology is a cost-effective way to precisely measure these clearances while effectively increasing safety for workers and the traveling public. The information gathered here can be used to:

  1. Update the NBI database,
  2. Routing of oversize permit vehicles
  3. Bridge Vertical Clearance Signage
  4. Maintain an Inventory of Overhead Sign and Bridge inventory.

The next set of graphics illustrates the typical technology solution utilized for these projects. It is composed of the Riegl VMX-450 LiDAR unit. This system can collect at rates up to 1.1 KHz (1,100,000 pts/sec) at a precision of 5mm. It collects points in a circular (360-degree) pattern along the right-of-way from 2 scanner heads facing forward and to the rear of the vehicle in a crossing pattern. The laser captures 3D points at a point density along the ground of approximately 0.001 feet at speeds up to 70mph. This scanner can be adjusted to scan at a rate that is applicable for the project specifications to limit the amount of data collected and to ensure that the resulting point cloud data is manageable. Right-of-Way imagery is also co-collected along with this LiDAR point cloud data. These images are used to identify appropriate attribution for each feature type being collected. They are also used to identify the real-world features that are measured and the exact location of the minimum vertical clearance for the Bridge or Overhead structure. The following graphic illustrates these concepts.

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Dual Head Scanners – One-Pass Technology Dense Point Clouds for Precise Measurements
High-Resolution Right-of-Way Imagery is Used to Identify Clearance Structure for Measurement
LiDAR Point Cloud of Same Overhead Structure with Clearance Measurements
Right-of-Way Imagery Fused with LiDAR Point Cloud for Photo-Realistic View
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Bridge Clearance Measurements (LiDAR Intensity View) Bridge Clearance Measurements (LiDAR Fusion View)
Local Orthophotography and LiDAR data co-registered to support the data extraction process
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Google Earth is used as a reference during compilation to verify bridge location and layout.

Once the data has been captured in the field, it is post-processed back in the office using a semi-automated approach. The Overhead Structure or bridge is classified in the point cloud using a manual process. The overhead points are classified into an “Overhead/Bridge” class. Then, the software automates the analysis of finding the lowest clearance point for a column of the data set.

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For example, the user can set a preference to search a radius of 1-foot and then the software will automatically find the closest ground point corresponding to the column of data. The minimum clearance value will be identified and recorded in the software for that column of data. This process is automatically repeated for the remainder of the structure until the minimum clearance point has been identified and located in the point cloud. The user can specify the output of the data as either a single minimum clearance of that structure, or can identify the lowest point vertically along a horizontal distribution of measurements. An example of this would be to return the lowest point per lane across a roadway for a particular structure. In conclusion, mobile LiDAR and Right-of-Way imagery are a safe and accurate way to measure the horizontal and vertical clearance of overhead and bridge structures. This methodology promotes a safe working environment for both the DOT worked and the traveling public. It is also a cost-effective way to collect large amounts of 3D point cloud data and process it efficiently as it is applied on a per-structure basis.


LiDAR News Magazine Now Available

Lidar News just posted its latest edition of their on-line magazine. We have another article in there titled “Utility Infrastructure Vegetation Management Using LiDAR, Imagery and GIS”.


Go see page 36 for the article!

From the editor: “We are always interested in receiving articles for the magazine. If you have something in mind please let me know and thanks for your ongoing support as we build this 3D community. Please tell a friend about us.”

LiDAR Editing Freak!

So, I have been working with the guys to keep my feet wet with LiDAR data editing so that I understand more about what it takes to prepare the LiDAR surface for delivery to the client or for an ortho surface.  I got my share of data and headed off to edit my surface…

Sounds pretty easy, right?  Well, it actually is as long as you know what to look for.  Our data for this project had a lot of low points in it – due mostly to the fact that we are shooting down stormwater grates in neighborhoods.  This creates low points in the data that is not indicative of the true terrain.  We usually filter these out using our filtering algorithms, but sometimes these points still exist in the data and need to be edited out.

The first way to identify a low point is to create a TIN of the surface.  If there are low points, the TIN will be dragged down by the surface and there will be a gaping hole in the surface.  Another way to identify these holes is to look at the color palette of the scene and if it does not have the usual distribution of colors – Red to Purple – there is a low point somewhere in the scene.

Low Point in TIN Surface

We can also see the low point using the “Profile” view – it can be seen below the surface.

Low Points Below the Filtered Surface

These points can be re-classified and removed from the Ground Classification and placed into the “Low Point / Noise” Classification and then the surface is modified.  Note the better distribution of the color palette for the scene…

Resulting TIN Surface

Finally, the resulting profile shows the points reclassified to the correct classification.  Repeat for each tile until complete!

Mobile/Airborne Co-Collection

We have a project close to home that will involve mobile LiDAR and Airborne LiDAR data capture.  We are looking at the absolute accuracies of the data and how they compare along a transportation corridor.  The goal will be to collect information related to a DOT’s Right-of-Way (fence to fence) and then collect high-fidelity LiDAR on the road surface that can be used for design/build projects.

The data is in the can and we are in the calibration phase of the project.  More to come over the next few weeks showing the sensor data and our project findings…