AEVEX is pleased to announce it has been recognized by HRO Today, a leading global HR network and content community, as winners of the North America Recruitment Team of the Year Award.
AEVEX Aerospace, a full-spectrum provider of innovative aircraft and advanced technological solutions across a broad range of government and commercial clients, announced today it has been selected as part of the SMX team to support the United States Africa Command (USAFRICOM) providing processing exploitation and dissemination (PED) services on the AFRICOM Reconnaissance Intelligence Exploitation Services (ARIES) task order.
This post, the first in a multi-part series of technical blogs, is dedicated to covering the details at the core of the AEVEX Aerospace Geodetics Products Geo-MMS product line: our navigation module.
AEVEX announced today an amendment of our existing FAA STC currently approved for the DHC-6-300 series to include the DHC-6-400 Series Twin Otter series aircraft. This new product allows the DHC-6-400 series to operate in the Standard Commuter Category with increased maximum take-off weight of 14,000lbs. (6350 Kg) when equipped with a Garmin G950 NXi/G1000NXi avionics suite.
LiDAR Systems: A Guide to Understanding LiDAR Sensors Function and Capability
Let’s talk about the various metrics used when discussing Geo-MMS LiDAR sensor characteristics – and how do we sort through the overwhelming information provided by LiDAR sensor manufacturers?
LiDAR Systems: Sensor Range and Precision
The range of a LiDAR sensor is often one of the key characteristics highlighted in any information you receive regarding that sensor. The sensor range is tested under laboratory conditions and is presented as the maximal range at which detection can be captured. For effective mobile mapping, however, the effective range is significantly less than the nominal range stated in a manufacturer’s datasheet. To simplify, we group mobile mapping range into three categories: tactical, mid-range and long-range.
Accuracy and precision are two different statistical concepts.
- Accuracy measures how close a range measurement (mean) is to the true distance of an object.
- Precision measures how repeatable are identical consecutive measurements to each other.
For 3D mapping cases, precision is critical for point cloud ‘crispness’ in the form of clean corners and defined features. Lower precision can result in fuzziness in the generated point clouds, making it difficult to discern important features.
LiDAR Systems: Sensor Ruggedness
The Ingress Protection Code (IEC 60529) provides guidance on a sensor’s resistance to ingress from particles such as dust and water.
- The first digit represents resistance to dust (1-6) – 6 is the most resistant
- The second digit represents resistance to water (1-9) – 9 is the most resistant
- Spinning sensors generally have the highest IP ratings (IP67 to IP69K)
- Raster scanning systems will typically have a slightly lower rating (IP65 to IP67)
LiDAR Systems: Sensor Architecture & Laser Type
In this series of blogs, we have broadly separated UAV-LiDAR sensors into two groups – spinning LiDAR and raster scan LiDAR systems. The system architecture of these two categories are fundamentally different. Raster scan sensors are the only kind that can be considered truly survey-grade. They were designed and developed for this function. These sensors possess higher return capabilities, giving them a distinct advantage for LiDAR mapping over the foliage.
Spinning LiDAR sensors were primarily developed for autonomous vehicle applications and robotics. Their usefulness also extends to affordable 3D mobile mapping. Installation on an autonomous vehicle illustrates the need for the highest level of Ingress Protection – to protect from power washing and exposure to harsh elements. The advantage of being lightweight and low-cost is attractive to many pursuing UAS-LiDAR projects. However, those achieving survey-grade accuracy and precision should always opt for a raster scan LiDAR sensor.
Spinning sensors typically utilize Edge Emitting Laser Diodes (EELD) while Raster Scan systems utilize a Fiber-Pulsed Laser. EELD lasers are a mature technology and offer reliable all-around performance. These sensors typically operate at the ~905 nm wavelength. Sensors with 600-1,000 nm wavelengths are not suitably safe for human eyes as the laser light can be focused and absorbed by the eye. As a result, sensors that operate in this wavelength have their power limited or capped to be in-line with safety regulations.
Fiber lasers are generally more expensive, power-hungry and operate at the ~1,550 nm wavelength. A 1,550 nm wavelength is eye-safe, so sensors with this wavelength can operate at high/full power. Because of this, they can be utilized at higher AGL altitudes. By allowing the operation of more powerful lasers without violating eye-safety regulations, it has a positive impact on the maximum achievable range. Sensor manufacturers who utilize 1,550 nm do so in order to pump a lot of power into the sensor.
Choosing the correct LiDAR sensor for your application and budget is an essential stage in setting up your UAV or mobile mapping system. For any questions relating to LiDAR sensors or your project in general, Request more Information. Already know the ideal sensor for your use-cases? Request a Quote today!
LEVERAGING ADVANCED REMOTE SENSING TECHNOLOGIES TO PROTECT OUR COMMUNITIES AND NATURAL RESOURCES
Wildfires consume millions of acres of land every year, destroying homes, communities, and infrastructure across the United States and around the globe. Fire seasons are getting longer, and fires are increasing in intensity as droughts increase and the effects of climate change become more pronounced. The economic and environmental impacts are devastating and costly, necessitating a closer study of wildfire causes and risk mitigation to better assess and minimize risk and damage.
With recent advancements in remote sensing technology, LiDAR data collection, and fire science, more and more solutions are becoming widely available for fire tracking and containment. In fact, AEVEX Aerospace collaborates with several fire departments – including the Orange County Fire Authority (OFCA) – on fire programs that enhance intelligence, surveillance, and reconnaissance technology for wildfires throughout the United States. The program lead for OFCA currently operates a FLIR 380-HD and an Overwatch TK-9 sensor onboard a King Air 200 aircraft. This system transmits video, maps, and images of burning fires in real-time to fire fighter control and command centers. Real-time analysis puts accurate information in the hands of crews and fire management personnel. For more information about this technology, please click here.
Considering the cost of operating such systems from manned aircraft, Geodetics (an AEVEX Aerospace company) is working on a UAV prototype equipped with several tightly coupled onboard sensors, allowing for data acquisition and modeling techniques to measure relevant variables for wildfire management and risk assessment. This UAV prototype is ideal for targeted wildfire management in reasonably spotted areas, and offers much lower overhead costs when compared to manned aircraft missions. An assessment of susceptible areas could produce models that characterize wildfire likelihood, intensity, and impacts. Measurable variables including topography, vegetation health (moisture content), land cover types, and relative temperature can aid in predicting the severity and intensity of wildfires. The specialized Geo-MMS (Mobile Mapping System) payload used in this study includes a long-range LiDAR sensor, RGB, multispectral, and thermal imaging sensors. The figure below outlines the system prototype with integrated sensor performance:
Geodetics approach to wildfire risk assessment involves a hybrid drone that can remain airborne for several hours during data collection. The proposed payload includes the long-range Optech CL-360XR LiDAR sensor, A growing multispectral imaging sensor, Sony α7R II RGB camera, and FLIR longwave infrared thermal sensor. Note that these sensors can be customized to meet different project demands and requirements.
These onboard sensors together provide accurate and reliable data for a wildfire risk prediction model. LiDAR data provides high-resolution, fine-scale measurements that can extract biophysical features of vegetation as well as creation of DTM/DEMs. The sensor measures the structural data, giving accurate and reliable information about topography, slope, and vegetation characteristics: canopy height, crown length, tree basal area, density, diameter, and dead tree density.
Originally published by Geodetics
How one AEVEX employee’s work on real-time mapping would influence Google Earth
When Don Burns was writing visualization software for NASA and supporting flight simulator development for McDonnell Douglas, Boeing, and the Army, he never thought that what started out as real-time 3D graphics in the ‘90s would one day turn into the technology that helps to power Google Earth. 30 years later, Google Earth is a household term, an application that is used for everything from education to remote sensing research, resource management, and even predicting disease outbreaks.
National Inventor’s Day was February 11. It’s a day to recognize the unique accomplishments that continue to further technology, science, and human knowledge. It’s also the perfect venue to recognize contributions – small and large – that influence individual careers and the world around us.
Don is currently part of the AEVEX Aerospace team as a Senior Software Architect, writing code for AEVEX’s Sierra software. Sierra is a 3D globe that integrates maps, aerial imagery, and terrain to help provide real-time data from air to ground for mission-critical and disaster mitigation situations.
“Many years ago, when I worked on the technology that would go on to become Google Earth, I never thought it would have such a significant impact on how I approached my work in the future,” said Don. “The relationship between real-time visualization, aerospace and flight simulation, and 3D mapping has evolved significantly over the years, and I’m proud to be a part of that.”
From coding for NASA, to graphical interactive terrain databases, and even to developing virtual reality attractions for Disney, Don’s career has followed a trajectory of innovation and out-of-the-box thinking.
“During my time at Silicon Graphics we were working on programming that modeled a 3D earth, including the capability to zoom in and see details. This was heavily based on the fast-moving, high-definition, real-time graphics we used in flight simulation and other forms of visualization, and our terrain databases evolved into whole earth databases.” Don said. “That work followed us into a start-up named Keyhole and a product named EarthViewer, which got the attention of Google. Google purchased Keyhole and EarthViewer became Google Earth. Google Earth enthusiasts may recognize the data format KML – in which the ‘K’ still retains the reference to Keyhole”
Just as Google Earth needed high-quality, interactive 3D graphics, first responders and military personnel also require high-performance graphics in real or near real-time frames. More than traditional mapping, digital interactive cartography helps track troops, mitigate natural disasters like fires and oil spills, and gain a detailed understanding of relevant topography.
“Working on the Sierra software for AEVEX has been very rewarding. By integrating interactive databases with live cameras, we are able to offer augmented reality that adds much-needed detail to surveillance, firefighting, tracking, and disaster mitigation,” said Don. “This technology is all airborne, so this robust technology also has to be optimized for size, weight, and power.
“I enjoy using my diverse coding and simulation background to continue the evolution of interactive mapping,” he added. “It’s exciting to see what’s next in the world of computer vision.”
And what is next?
“It’s all about artificial intelligence, specifically computer vision,” Don said. “This enables computers and computer systems to gather meaningful information from visual input like images, videos, and maps. This is very compute-intensive, so as the need for this type of data grows, so does the need to evolve technology, graphics, and databases. But the more effort we put into inventing the future of mapping and simulation, the safer we can make the world. I imagine a future where fires are mapped, tracked, and predicted from the start. Where oil spills are quickly contained with predictive data. Where troop movements are accurately tracked and assessed.”
Inventions – both big and small – and contributions – both long- and short-term – will guide the future of these technological developments. Here’s to many more innovative ideas!
LiDAR Sensors for UAV / Drone Application
When selecting the right LiDAR you must consider the various parameters that define the performance of the LiDAR sensor. These parameters are summarized in the table below.
If you are an expert in LiDAR scanning, you can probably stop here. Otherwise, this article provides assistance in selecting the right LiDAR sensor for your application. There are several approaches to narrowing down the right LiDAR sensor. The first, and probably the easiest, is basing your decision on price. Another approach is based on considering factors such as operational environment, the weight of the sensor which impacts the flight duration, etc. There is nothing wrong with these approaches; however, here, we tackle this question from a different perspective. In our approach, we consider flight altitude, required resolution and the application. The reason we drop accuracy from consideration is that in most cases, regardless of the LiDAR sensor, the desire is the highest accuracy.
In LiDAR mapping, the flight altitude is a key parameter in picking the appropriate sensor. If you can fly below 60m AGL, the tactical-range sensors are appropriate. For altitudes higher than 60m, you must consider either mid-range or long-range LiDAR sensors as shown below.
The projection of a single laser beam onto the ground yields its resolution. The resolution is a function of the flight altitude, scan rate (frequency) and the angular resolution. A minimum resolution for the tactical/mid-range LiDAR is about 5-10cm@30m; while for long-range LiDAR’s is about 2-3@100m. The frequency and angular resolution of the tactical-range and the mid-range LiDAR sensors are in the same range. Thus, if you need a similar or higher resolution at higher altitudes, then you must use a long-range LIDAR sensor.
The last parameter to be considered is the application. For applications, such as agriculture, canopy classification, forestry and forest planning, topography (DEM/DTM) and archaeology, where canopy penetration and ground returns are required, the number of channels and returns provided by the sensor will help narrow your selection. Velodyne LiDAR sensors currently offered with Geo-MMS provide up to two-returns, the Quanergy M8 LiDAR provides up to three-returns, and the Teledyne-Optech LiDAR sensors provides up-to 4-returns. For applications such as powerline and transmission inspection, railway infrastructure inspection, BIM/architecture, query/open-pit/mining, etc. the number of returns is less important.
The Geo-MMS product suite is available with a wide range of LiDAR sensors. We classify LiDAR sensors into three categories: tactical, mid and long-range as illustrated below:
Road Surveying using Geodetics’ Geo-MMS LiDAR Suite of Products
The purpose of this blog – the second in a two-part series – is to continue to explain the capabilities of Geo-MMS LiDAR payloads for road surveys and highway scanning. We will explore the wider workflows surrounding the digital mapping of transport infrastructure and associated assets. In case you missed Part 1 of Road Surface Mapping with LiDAR, it can be found here.
Expected increases in US infrastructure spending over the next few years will undoubtedly incorporate heavy capital investment in new transport projects. However, what is arguably more important is the maintenance of existing transport infrastructure. Accumulated road damage carries a high cost to address. Oftentimes, as soon as damage in one location is addressed, a new problem area is identified. Continuous ‘damage repair cycles can be avoided by identifying areas of weakness in the network and addressing them before the damage grows to the extent where manned crew deployment is needed to address the issue, causing disruption to local traffic and increasing costs further. With a lack of high-quality data, inconsistencies in damage reporting, and an overall lack of adequate prioritization, it can be difficult to provide an integrated solution.
A strategic documentation plan for all road networks (preventative maintenance) is more economically attractive than the passive approach often taken by many transport planning departments. 3D scans of the full transport environment feed into other important infrastructure inspections, including road surveys that include assessment of pavement conditions, bridge inspection, roadside powerline assessment, etc. Transport planners need to work smarter rather than harder by leveraging the available technology, achieving more results with less manpower.
LiDAR scan data can be enhanced by spherical imaging of the surrounding landscape in a 360° frame, the same frame a LiDAR sensor operates within. Ladybug, an RGB camera designed for mobile mapping, captures spherical 360° panoramic views of the environment. It also merges images from six internal cameras. Using the Geo-MMS Navigator, images from all six Ladybug cameras are geo-tagged, allowing geolocation features to be registered in the images. The combination of LiDAR and spherical imaging provides a powerful tool for powerline and transmission line monitoring, bridge inspection, road surveys, etc. To facilitate this multi-sensor integration, Geodetics provides a mounting system specifically for this setup, as shown in Figure 2. This bracket has two degrees of freedom such that it can be mounted on any vehicle regardless of orientation and height.
This provides a second dataset, used for detailed visual assessment of features of interest identified through LiDAR, including road surface markings and pavement cracks. Full panorama images and 3D scan data, coupled with simultaneously collected precise position, orientation, and timing data combining produce multifaceted, high-resolution maps. These maps provide walk-through simulations – an innovative solution – one which will become a more common practice in the future as the value of manual inspections gradually decreases and digital inventories are required for documenting all infrastructure (and associated assets). The collected data is most valuable when assimilated into existing infrastructure databases which help define strategies for future developments, maintenance schedules, and transport planning, as exemplified in BIM.
With all Geo-MMS LiDAR payloads come seamless capability to switch between ground-vehicle and UAV platforms. Additional opportunities arise when integrating ground-captured and UAV-captured point cloud data in the same viewing frame. Within the realm of road-surface mapping, these applications can extend to disaster assessment after natural disasters, aging facility inspection, progress inspection on-road remodeling projects, etc.
While terrestrial laser scanning can produce superb accuracy, obvious limitations are the duration of set-up and scan time for a relatively small section of roadway. Coupled with the additional labor hours needed to perform this work, accuracy may be high, but at the expense of time, safety and cost-efficiency. A combination of terrestrial, mobile, and UAV data may be the most comprehensive option, but the majority of the workload can be handled by a mobile scanning device. This can be installed on the rear of a car, SUV, ATV, pickup truck, or any other vehicle robust enough to support the temporary installation. The popularity of mobile scanning has increased where software developers have focused on this specific niche and application – TopoDOT software being one such popular example. Software options can automatically create cm-level road surface models, identify and export curbs, railings, utility poles, encroaching vegetation, and other features of interest in the transport infrastructure which can be extracted for further analysis in GIS and CAD programs. Meshing including TINs (Triangulated Irregular Networks) can be performed using widely employed automated algorithms that generate 3D statistics regarding the shape, volume, and frequency of structural anomalies.
What many are seeing across the industry are specialized scanning systems developed specifically for the goal of road/highway maintenance across transport and infrastructure industries. As pioneers in Defense-grade navigation technology and sensor integration, Geodetics is the trusted source for cost-effective and customized LiDAR mapping solutions in the air, on land, and at sea.
Originally published by Geodetics