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Jason Amadori's LIDAR GIS BlogArchive for mobile lidar
Mobile LiDAR to Support Positive Train Control
DTS/Earth Eye just completed a positive train control (PTC) project for a national train company who was evaluating the differences between Airborne LiDAR and Mobile LiDAR to support the collection of PTC data. They are currently collecting airborne data for approximately 15,000 linear miles of rail. In certain areas, the airborne data does not provide enough fidelity to accurately map the rails or the asset infrastructure that support the railroad operations.
From Wikipedia – “The main concept in PTC (as defined for North American Class I freight railroads) is that the train receives information about its location and where it is allowed to safely travel, also known as movement authorities. Equipment on board the train then enforces this, preventing unsafe movement. PTC systems will work in either dark territory or signaled territory and often use GPS navigation to track train movements. The Federal Railroad Administration has listed among its goals, “To deploy the Nationwide Differential Global Positioning System (NDGPS) as a nationwide, uniform, and continuous positioning system, suitable for train control.”
The project involved the collection of Mobile LiDAR using the Riegl VMX-250 as well as forward-facing video to support PTC Asset Extraction. The system was mounted on a Hi-Rail vehicle and track access was coordinated through the master scheduler with the Railroad company. Once we had access to the tracks, we had one shot to make sure the data was collected accurately and we had complete coverage. All data was processed on-site to verify coverage and we had a preliminary solution by the end of the day that was checked against control to verify absolute accuracies. We collected the 10-mile section of rail in about 2 hours and this timing included a couple of track dismounts required to let some freight trains move on through.
The following graphics illustrate the point cloud coverage colored by elevation (left) and Intensity (right).
Mapping the rails in 3D was accomplished by developing a software routine designed to track the top of the rail and minimize any “jumping” that can occur in the noise of the LiDAR data. Basically, a linear smoothing algorithm is applied to the rail breakline and once it is digitized the algorithm fits it to the top of the rail. The following graphic illustrates how this is accomplished – the white cross-hairs on the top of the rail correspond to the breakline location in 3D.
So, back to the discussion about Airborne PTC vs Mobile PTC data. Here is a signal tower collected by Airborne LiDAR. The level of detail needed to map and code the Asset feature is lacking, making it difficult to collect PTC information efficiently without supplemental information.
The next graphic shows the detail of the same Asset feature from the mobile LiDAR data. It is much easier to identify the Asset feature and Type from the point cloud. In addition to placing locations for the Asset feature, we also provided some attribute information that was augmented by the Right-of-Way camera imagery. By utilizing this data fusion technique, we can provide the rail company with an accurate and comprehensive PTC database.
This graphic shows how the assets are placed in 3D, preserving the geospatial nature of the data in 3D which is helpful when determining the hierarchy of Assets that share the same structure.
One last cool shot of a station with all of the furniture, structures, etc that make it up – pretty cool!
Mobile LiDAR and Cross-Slope Analysis
DTS/EarthEye just completed a 9-mile mobile LiDAR scan of I-95 here in Florida and provided one of our partners with cross-slope information in a period of days. The data was collected with our buddies at Riegl USA using their VMX-250 mobile LiDAR. This information will be used to generate pavement resurfacing plans for the Florida Department of Transportation (FDOT).
This project shows the value that this type of project can provide to the end user on both sides of the fence.
First, the paving contractor can use this data to develop their 30% plans for submittal to FDOT when bidding on a resurfacing or re-design contract. Having accurate and relevant data related to the roadway’s characteristics gives the paving contractor an edge over the competition because they know what the field conditions are before preparing an over-engineered design specification. This happens all of the time because the detailed field conditions are unknown while they are preparing their plans and they only have historical information to work from.
On the other side of the fence resides the FDOT. They can benefit from this information because if they can provide this detailed information as part of a bid package, they can reap the benefits that are gained from better information. If all contractors have the detailed as-built information (or in this case, accurate cross-slopes), they can all prepare their submittals using the same base information. This will provide the FDOT project manager with more accurate responses based on true field conditions, resulting in more aggressive pricing and decreased project costs.
Here are some screenshots of the information.
LiDAR Data Viewed by Intensity and Corresponding Cross-Slope Profile
Once the data has been collected and calibrated, we generate cross-slopes at a defined interval and export those out as 3D vectors.
These vectors are then symbolized based on their cross-slope percentages and exported as a KML file for ease of use.
Although this is a pretty simple step, the presentation of the data in Google Earth makes it easy for the end-user to visually identify problem areas and design the corrective actions according to field measurements.
Getting Ready for Some Rail Data
We are going to have some rail data collected here soon and I’ll post the results as soon as I get them. We’ll be collecting a 17-mile section of track in the DC area, so it will be full of urban canyons, tunnels and highway overpasses. At least we’ll know how well the equipment will be performing in the field because this will be a true field test of the equipment.
We just finished getting e-Rail certified to work with the railroad, so now it is time to provide some value to this market. They are looking to collect features that will support Positive Train Control as well as giving them engineering-grade information that can be used for maintenance & rehabilitation efforts.
Rail Intensity
Our goal will be to collect all of this information in one pass and give them tools to automate the extraction of other features such as the rail, ballast, gauge, etc. Data collection will happen next week and we’ll have some results to share at that point.
Rail LiDAR with Elevation
Rail Data Themed by Elevation
Rail Data with Intensity
Mobile LiDAR to Support Roadway Resurfacing
We just completed a mobile LiDAR project that was designed to support a roadway resurfacing project in Orlando. The project was centered on the use of mobile LiDAR to generate roadway profile data that Engineers could use to design a resurfacing project. Obviously the data would need to be accurate and we were able to hit the mark and best of all – prove it!
Overview of SR417 Project
We collected the data using the new Riegl VMX-250 mobile LiDAR unit using a single pass in the north and southbound directions. We only required one pass in each direction to collect the road data which makes it very efficient from the data collection standpoint. In the past, we had to collect “strips” of data and then “sew” them all together during the calibration process. In this case, we took the opposing (NB and SB) strips and calibrated them relative to one another and then they are brought down to control as a final step.
LiDAR Coverage by Flight Line
Most of our clients are interested in the overall accuracies of the data, so we have built accuracy assessment tools that make it easy to review the LiDAR against survey control. The tool is simple to use and allows us to sort the results and dig deeper into the least accurate points to see why there might be discrepancies in the control vs the TIN surface.
417 Accuracy Control Report
For this project, we achieved an RMSE of .0525 ft – calculated by comparing the control elevations (Z) against the TIN elevations (Z TIN). This is important because we can check the point cloud against known control that was collected throughout the project and provide detailed information about the accuracy of the data.
Once the data has been calibrated sufficiently, we can then generate all of the derivative products for this project. We generated the following data for our roadway engineers:
- Pavement Cross-Slope
- Shoulder Cross-Slope
- 3D Roadway Markings
- Edge of Friction Course
3D Vector Data
This data set also supports detailed engineering analysis related to guardrail height above the roadway. This is an important factor to consider because there are specific standards that define where the guardrail is placed, more specifically, its height above the roadway, to corral vehicles that end up impacting the guardrail in an accident situation. The following graphic displays how this measurement can be made in the point cloud data.
Guardrail Height Measurements
Tunneling through the Trees
Just finished collecting a site for a project with some massive overhead trees. This wreaks havoc on the GPS signal and validates the importance of having a good Inertial Measuring Unit (IMU) on board to carry the trajectory of the vehicle during the GPS outage. This section of road is a virtual “tunnel” of trees which makes it difficult to nail the accuracy specification of 0.1-foot with GPS alone!
- Elevation Data of Roadway Obscured by Trees
The next graphic shows the same area displayed by Intensity:
Intensity Image of Canopy Road
Here is a 3D Version of the Elevation point cloud:
3D Elevation Point Cloud
And here is an elevation profile of the same area:
Elevation Profile of 3D Point Cloud
Over the next couple of weeks, we will be creating 3D vectors from these point clouds that will be used to determine the geometric characteristics of the roads. This information will then be used as parts of safety audits related to the following information:
- Radius of Curvature
- Horizontal Curve (Cross-Slope)
- Vertical Curve (Grade)
This data will then be used with crash data to determine if a road needs to be re-aligned based on its geometric characteristics. For example, a tight curve with a high speed may be contributing to crashes on a segment of road. This information can be used to understand precisely how a road is constructed and functioning in the real world.
Airborne Accuracy Assessment
We’re moving along with one of our mobile/airborne projects and we just received an independent control assessment and the results are looking great! We have achieved a project-wide RMSE of about .1112 feet for the airborne portion of this project – which is pretty good for airborne…
SR417 Project Extents
The way we check this is by loading the ground control and LiDAR data into our data viewer and then running a control report against the data. Basically, we’re intersecting the ground control with the TIN model of the ground class of points. The Z values are checked against one another and the difference is calculated. These results are then used to create an RMSE for the project based on the control results.
This is a good way to get an idea of how well the data has been calibrated in terms of absolute accuracy. Once we get a control report, we typically sort by the worst result and then start examining the control and the surrounding terrain. The graphic below shows how we can sort the results and then “Go To” the control point in question.
Zoom to Control Point to Examine Local Conditions
We can see how the point is being assessed against the terrain. Sometimes, there is a blunder in the terrain model and we might be able to edit the terrain to make sure it is the true ground surface. Elevated objects such as trees can influence the accuracy assessment, but sometimes, it might be as subtle as a gutter drain, as seen in this next graphic. The profile view of the drain cross-section shows how the terrain is influencing the accuracy assessment.
Control Point Cross-Section Showing Uneven Terrain
The goal in the future will be to collect all control on uniform areas that are not subject to sudden terrain changes. This will ensure that the TIN correctly models the surface that is being checked for accuracy. The next graphic shows the actual results for this project…
Control Report for Airborne Data
Our next step will be to check the control against the mobile data. We should have that in about a week or so…
Cool Mobile / Airborne Project
We have another pretty neat mobile and airborne LiDAR project here in Orlando and the data has just started to materialize. Over the next couple of weeks, I’ll be posting the data and discussing some of the neat things about it all. I’ll first start by laying out the project and what we’re trying to do with it and then start discussing the results as we get into the analysis portion of the project.
The project is located in Orlando, FL down near the southern junction of I-4 and SR 417. We are supporting a resurfacing project that is about 5 miles in length. The goal of the project is to see if we can save the design engineers time and money by using mobile LiDAR to collect the corridor and give them an Engineering-grade model of the existing paved surface. They will use this information to design the resurfacing project and hopefully save on materials in the field by using an accurate model of the existing conditions.
Traditionally, this information was collected through the use of Low-Altitude Mapping and Photogrammetry (LAMP) or by surveying cross-sections along the project. Both of these technologies work and are proven to be accurate, but nothing can beat using a digital terrain model built from millions of points, right? That is what we’re going to use and we are just getting some preliminary data from our partners in this project, Riegl Corporation.
We have a sneak peek of their new scanners, the VMX-250. This scanner is pretty amazing and has caused us to re-write our software to handle the large amounts of data it generates. The graphic below shows an ulfiltered data set of a portion of the project.
417 Project by Drive Line
The colors represent each drive line captured in each direction. There are a total of 2 drive lines here, each drive line is collecting data from 2 scanners. As you can see, there is a bunch of “junk” in there, but if you look in 3D mode, you can see that the drive lines are calibrated pretty well.
417 by Drive Line in 3D
All of the data above was collected in 2 passes with 2 scanners which is pretty amazing. I have all 4 loaded up and the data size is over 10Gb for about a 1-mile portion of the project. So, as you can tell, there is a ton of data to review, edit and mine for this project!
417 by Intensity and Profile
I’ll end this post with the graphic above showing a cross-section of the road and a view of it by intensity. We’ll be working with this data over the next few weeks and as we get some results, I’ll post them here!
More Mobile Data
We’ve wrung out the calibration issues with our mobile LiDAR and we have a solid solution that we can hang our hat on. We have achieved RTK-equivalent accuracies for short runs of less than 5 miles and today, we’re heading out to see how well the accuracies hold up for a 30-mile run.
No worries about having this ready for prime time today – most projects we’re dealing with are only 3-5 miles right now. Our goal is to figure out the best way to keep the GPS solution in check for long runs to avoid post-processing and calibration issues on the back-end of the collect. Since most of the required measurements are “relative”, the LiDAR data is good, but we are striving to crack the “absolute” accuracy nut – and that involves a solid calibration of the equipment. We’ve always known this, but actually “doing” that is a different story!
This is a 5-mile run that we collected here locally. The data has been filtered to a bare pavement surface and we have run cross-sections every 5 feet. Each cross-section can be exported to CAD, GIS, Microstation, etc and used to build pavement resurfacing design drawings.
We have been able to make the processing of this data “semi-automated”. Basically, we have to draw in the breaklines which typically correspond to the pavement stripes. These define the lanes of travel and then we do a slope calculation (relative measurement) from one breakline to another – effectively calculating the cross-slope percentage for each lane. We’re also exporting out a tabular format so clients can use that information to verify the values against a pavement design spec.
Slope 1 refers to the left lane (southbound) and Slope 2 refers to the right lane of travel (northbound) and all slopes are percentages.
Mobile LiDAR Update
Here’s some LiDAR data from the City of Charlotte Pilot project. The images below shows the different data stored in the point cloud. This information is useful to our team of processors during the data extraction phase. For example, the image below shows the LiDAR point cloud themed by intensity. The intensity information can be used to extract different types of vector data such as the road centerline, edge stripes, pavement markings and other special pavement markings. The intensity values can be used to assess the condition of the markings based on its reflectivity. Basically, the markings are reflective or they’re not and that is a representation of their condition.
Pavement Markings from LiDAR Point Cloud Intensity
The point cloud can also be used to generate contours. The image below shows contours that are 0.1-foot. A trained eye can see the usefulness of this data in that a roadway transition is occurring in this area.
Elevation Data Displaying 0.1-foot Contours
The next image shows a roadway profile from the same area. This profile information can be used to generate cross-sections at any interval along the roadway.
Elevation Profile
Airborne LiDAR Update
We are operational!
Aircraft – Check!
LiDAR – Check!
Aerial Photography Camera – Check!
Hyperspectral Camera – Check!
LiDAR Data Colored by Elevation
We’re operational and have a ton of data in the can and ready for processing. Our data sets include samples from residential communities to transmission powerlines to unmentionable clients who have some interesting needs! One of the biggest hurdles has been developing our own viewing software that we can deliver with these large datasets so that our clients can manage their deliverables. The goal is to build a piece of software that is lightweight and easy to maintain code-wise, while building tools that clients can use to streamline their business processes.
3"-pixel Aerial Photography
Keep watching here for data samples and updates to our software!
