An Inside Look at a Mobile Data Collection Project

Recently Mandli Communications, Inc. was subcontracted by MACTEC, an Atlanta-based engineering, environmental, and construction services firm, for a contract with the Federal Highway Administration – Office of Asset Management. The primary goals outlined by FHWA for this ongoing project are to to define a consistent and reliable method of assessing infrastructure health with a focus on pavements and bridges on the Interstate Highway System, and to develop tools to provide FHWA and State DOTs ready access to key information that will allow for a better and more complete view of infrastructure condition and health nationally.

Our portion of the project included collecting approximately 864 miles of data including IRI (International Roughness Index), rutting, pavement imaging, GPS, and right-of-way imaging data across the I-90 corridor through South Dakota, Minnesota, and Wisconsin. In order to collect an even more comprehensive data set for the route, we decided to capture LiDAR data as well.

Over the past few years, Mandli has worked steadily on creating a vehicle-based LiDAR system that can collect accurate roadway data at highway speeds. At first we utilized the HDL-64E LiDAR sensor from Velodyne. Working closely for over two years with the manufacturer we were able to modify the sensor and customize the data output to match the potential needs of our clients.

Recently, we have moved to utilizing two Velodyne HDL-32E sensors during collection, instead of a single HDL-64E. We have made great headway with our mobile system, performing large-scale data collection projects for several state DOTs. In Texas we have collected over 44,500 miles of LiDAR data, and in Tennessee we collect over 15,000 miles of LiDAR data annually as part of our current contract. This data is in turn used by the Departments for a variety of applications, from clearance measurement to asset inventory to three-dimensional visualization.

Mandlis expertise is in imaging, positional, and pavement data collection. In order to get the most out of LiDAR technology we realized that we would have to be able to integrate the new hardware into our existing data collection platforms. For our State DOT projects we collected synchronized right-of-way imaging and GPS data along each route. The imaging provides a much-requested visual reference to the LiDAR data, and with the GPS we are able to assign positional coordinates to each individual point in the LiDAR point cloud. When synchronized with the LiDAR sensor both of these systems increase the value of the collected data for our customers far beyond what each individual dataset is worth.

This project for MACTEC marked the first time we deployed one of our pavement data collection vehicles with an integrated LiDAR system. This was also the first multi-state contract where Mandli utilized the new Laser Crack Measurement System (LCMS) from Pavemetrics. Like the LiDAR sensor, the LCMS is a three-dimensional data collection system that is able to collect 3D profiles of the surface of the roadway. It also collects 2D downward-imaging data. This can be used to identify a wide variety of pavement characteristics, including distress, rutting, macro-texture measurements, potholes, sealed cracks, joints in concrete, tinning, and more.

The collection vehicle also included a Road Surface Profiler for the collection of IRI, faulting, and texture data, as well as the previously mentioned imaging and positional systems. However, with the addition of the newer LCMS and LiDAR sensors to our vehicle, this was our first project where we synchronized the collection of two separate 3D data sets coming from two different sensors.

While this was a very exciting project for us, it also presented logistical hurdles, mainly the collection of two large 3D datasets. Each sensor on their own produces a relatively large amount of data, and when synchronized with each other, as well as three separate subsystems, this became one of the larger per-mile data collection projects that we have performed. Fortunately, for this project we were able to utilize the same methodologies we have in the past for integrating several imaging, positional, and pavement systems together. These methodologies allowed our field technicians to view all of the datasets simultaneously and in real-time during collection while checking for any inconsistencies in the data.

Not only were we able to collect a comprehensive dataset for our customer during this project, but we were also able to successfully test the integration of our LiDAR system with our pavement data collection vehicle. As LiDAR technology has continued to improve we have been looking at it less as a separate system and more as a component of a larger data collection solution. We are continually excited to be working with both the hardware manufacturers and our customers to find new ways to make use of this new technology.

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