Tuesday, September 20, 2016

By the Numbers: Positioning and Navigation


  • 4:  Number of individual positioning systems equipped on each of our 4 Mobile LiDAR systems: 1 dual antenna GPS/GNSS; 1 military-grade Inertial Measuring Unit (IMU); 1 Distance Measuring Instrument (DMI); and 1 GPS for vehicle navigation.
  • 200:  Frequency at which the vehicle’s position is updated every second (Hz).  The IMU fine-tunes initial positioning generated by the dual-antenna GPS/GNSS by combining readings attitude (pitch, yaw, roll) calculations with distance traveled (DMI) to produce an accurate position at 200 Hz.
  • 1:  Number of IMU’s onboard each Mobile LiDAR system.  One of our four IMU’s is also used by the U.S. Military as part of the guidance system of a Patriot Missile.  That system is regulated by the U.S. State Department through the International Traffic in Arms Regulations (ITAR) – needless to say, there’s a little bit of red tape to clear if we want to take that unit out of the country.
  • 12:  Number of miles of subterranean mine shafts surveyed during a previous project.  The combination of the IMU and DMI allow our systems to perform accurate collections during sustained GPS-outages using dead reckoning.  
  • 1,024:  Number of wheel rotation measurements performed every second.  Our DMIs are directly affixed to the vehicle’s wheel to ensure reliable readings. Due the inherent positional errors with GPS the DMI is not only used to indicate accurate distance traveled, but also to alert the system when the vehicle is stopped - called the "Zero Update".
  • 2:  The fluctuation in tire pressure (lbs.) which will result in inaccurate distance measurements.  During a collection, the DMI scale factor (a function of the tire's circumference) is constantly monitored and "calibrated" using other systems, including GPS.



Wednesday, September 7, 2016

Circling the answer through RANSAC

Here in Michael Baker's Mobile LiDAR Center of Excellence, we are routinely challenged with developing new ways to extract the most from our data. Our focus is to minimize the amount of human interaction required to extract a relevant piece of data from the voluminous point clouds we collect. Understanding our necessary and recurring requirements within automated processes is important to us for the types of products we deliver. Knowing requirements helps us to determine if we are in a “buy versus build” decision for a particular software automation task. Many software companies are developing robust commercial software tools for feature extraction from LiDAR point clouds. However, the rights to modify any commercially developed software and inherent intellectual property and licensing costs can make the “buy” decision onerous and costly for us.

Understanding the likely and underlying algorithms we’d choose to employ in any software we look to build, or buy and license is vital. As engineers, surveyors and subject matter experts with a deep awareness of LiDAR data and its processing we know what we want and need for automation. Knowledge and necessity are not mutually exclusive. They should optimally align to produce the best solutions. That Rolling Stones lyric comes to mind. Yet, we don’t agree with the song’s implied result when it comes to software: “You don’t always get what you want, but you get what you need.”

In the best case, we get what we want and also need as it comes to our requirements for LiDAR processing. Too many needs are fulfilled inefficiently with commercial software. We must avoid “kluge approaches” when fulfilling a specific software or task sequencing approach, so in house development allows us to fulfill these needs specifically and efficiently in our LiDAR processing.

As an example let’s examine how we might fulfill a very repetitive task while using a computer to find the precise center, radius and circumference of a circle within the chaos of thousands of LiDAR points in a point cloud through a combination of geometry and algorithm design.

RANSAC (Random Sample Consensus) is an iterative based outlier detection method. The basic principle of which has been evolved to work with geometric equations allowing the system to determine the best fit circle within the LiDAR point data as well as locating the center of the best fit circle. The system creates “slices” through the points at a specified thickness in the vertical plane then determines the best fit circle and center point for each slice. Once we determine the location of a phenomena that might represent a circular object, we can use an automation tool to facilitate automatic extraction.

The advantages of such a tool include:
  1. Ability to calculate the taper of any circular object ( columns, posts, poles, water tanks, etc.) and project the taper to a point that may be obscured by an object in the LiDAR data
  2. Determining if an object is leaning by analyzing the center points of all the slices along the height of the object
  3. Reducing the human error of manual attempts to extract the circle and center points.
We are continuously striving to expand our LiDAR data extraction capabilities at Michael Baker while appropriately making the correct buy or build choices with any software we utilize. When working with repetitive processes on tens of thousands of single functions of extractions per week, the simplest software process is better and more efficient for us. Geometric shapes are precise forms. They are mathematically consistent. Circles represent just one shape of the most basic and important forms of geometric objects. Squares, spirals, triangles are also basic forms. Extracting basic forms efficiently and cost effectively is very important to us.

We continually focus on getting everything right with one right extraction process at a time. This RANSAC system of processing is one of many developments that we will continue to exploit which sets us apart, ensuring Michael Baker International is the first name in LiDAR.

Regards,
Sandor

Sandor Laszlo, PE is a Software Engineering Supervisor with Michael Baker's Mobile LiDAR Center of Excellence.  Sandor's current focus is on the development of semi-automatous systems for extraction of features from LiDAR point cloud information. 

Monday, September 5, 2016

Happy Labor Day!

Today we celebrate the contributions of the American Worker to our country’s success.  All of our U.S. offices are closed this Labor Day in honor of those that have played a pivotal role in building our nation’s infrastructure. As we pack our whites and celebrate the unofficial end of summer, we’re also planning our continued deployment of innovative solutions to ensure “We Make a Difference”.

Wednesday, August 31, 2016

By the Numbers: Lasers and Cameras

  • 168:  The number of Megabytes of data created EVERY SECOND by each of our Mobile LiDAR systems when we fire the lasers and cameras at their maximum rates.
  • 150:  Weight in pounds of each of our 4 Mobile LiDAR systems.
  • 35:  Weight in pounds of each of the two onboard LiDAR sensors that comprise each of our systems.  The solid, machined-aluminum chassis of each sensor doubles as a giant heat-sink to dissipate internal heat generated by the sealed unit.
  • 2:  Number of hours it takes to transfer and test one of our Mobile LiDAR systems from its regular vehicle mount to our boat or UTV.
  • 1.2 Million:  Number of discrete laser shots each of our Mobile LiDAR systems is capable of generating EVERY second. 
  • 4.8 Million:  The astonishing number of measurements calculated by each LiDAR system every second.  Each discrete laser shot can result in up to 4 returns (1st, 2nd, 3rd, and Last).
  • 5:  The number of primary camera units deployed with each Mobile LiDAR system.  Four cameras can maneuvered to a myriad of locations along the vehicle and can simultaneously fire up to 2 frames per second (fps).  The 5th camera is our Ladybug 360 spherical unit, which can fire at a rate of 3 fps.
  • 6:  The number of individual cameras that are housed in the Ladybug spherical camera system – software automatically stitches the individual images together for spherical viewing. The versatile spherical camera unit also produces individual calibrated images that can be used to colorize the corresponding LiDAR point-cloud, or loaded into 3rd-party applications (such as our Orbit WebViewer) for overlay of the point-cloud.

Friday, August 26, 2016

Picture of the Week: Orbit RGB Point Cloud

A couple months ago we discussed how we served LiDAR data in Orbit GT through the Baker Enterprise Architecture for Spatial Technology (BEAST).  Below are some images of how we are using RGB colorization in Orbit.  The colorization of the point cloud has evolved from using two cameras with our first Optech Lynx v200 system to the integration of the LadyBug spherical camera - revisit our post from 2010 called Coloring the Cloud.

The RGB colorized point cloud is seamlessly integrated with the LadyBug panoramic imagery after calibrating in Optech's LiDAR Mapping Suite (LMS) software.
Escaping the view using the panoramic imagery, the user can work directly with the colorized point cloud.
The blue points represent camera exposures that the technician can "step into" for feature extraction and review of the panoramic imagery.
Cheers!
Stephen