Trimble has introduced an all-in-one device for mobile communications and surveying data collection — the Trimble Slate Controller. The Trimble Slate Controller combines the convenience and ease-of-use of a smartphone with rugged durability. Optimized for Trimble Access field software and the Trimble R4 GNSS receiver, the Trimble Slate Controller supports a surveyor’s everyday workflows.
“Surveyors require mobile, rugged solutions that can readily withstand and perform in the toughest of conditions,” said Erik Arvesen, vice president of Trimble’s Survey Division. “With the introduction of the Trimble Slate Controller, we are providing a rugged handheld device designed to run survey workflows while also delivering the capabilities and convenience of a smartphone.”
Offering voice, SMS text, and 3.75G cellular data transfer capabilities on GSM cellular networks worldwide, the rugged Trimble Slate Controller enables enhanced connectivity in the field. Its wireless communication capabilities keep surveyors connected to the office. An integrated 8-megapixel camera offers enhanced job documentation and point attribution by providing geotagged, high-quality digital photos.
The Trimble Slate Controller’s slim, ergonomic design is easy to hold while its screen provides superior sunlight readability enabling all-day use by survey professionals. Designed to withstand even tough conditions, a 4.3-inch capacitive touch Gorilla glass display covers the entire front surface, increasing readability without sacrificing durability.
Trimble Access field software available on the Trimble Slate Controller offers a variety of features and capabilities to streamline topographic, stakeout, control and other surveying applications. Partnered with Trimble Access and the Trimble R4 GNSS receiver, the Trimble Slate Controller provides a dedicated GNSS solution that is effective for both real time and post-processed GNSS surveys, Trimble said.
Three weeks ago I had a chance to visit the offices of TerraGo Technologies in Atlanta. I first used their technology in the early 2000s, when I was the GIS manager for the Atlanta Regional Commission. For those of you that may not remember GIS and mapping before GeoPDF maps, it was a real challenge to provide interactive maps to users outside your organization. A GIS author had to ship the data layers, attribute tables, symbol sets and layouts as a package to a user who had to have compatible GIS software. One then had to hope that the user pointed to each data layer correctly and had a good sense of cartography to create maps that told the story. If the user chose inappropriate lines, colors or symbology, the resultant map could look terrible at best, misleading at worst.
Esri tried to solve the problem with Map Publisher which maintained the author’s cartography, but if any data layers were corrupted or not pointed to correctly, the map failed. GeoPDF maps solved that problem since all the data layers and even the map layout/cartography were preserved as one single PDF file that could be read and interactively queried by anyone using a simple Adobe Acrobat reader. A user could turn layers on or off, zoom in/out and query attributes. TerraGo also added the TerraGo Toolbar that enhanced the map with measurements, geo-locations and the ability to collaborate with others on the same GeoPDF map.
GeoPDF maps and imagery were quite a leap in map publishing capability and soon became ubiquitous with key federal users and a de facto standard for map publishing within the Department of Defense (DOD) and the U.S. Geological Survey (USGS). Anyone can download many GeoPDF maps free of charge, including U.S. topo maps from the USGS Store.
For federal and DOD users, the U.S. Army Geospatial Center (AGC) has published more than 200,000 maps of locations around the world. Some samples, including 3D GeoPDF maps, can be viewed by the public. In 2009 TerraGo opened “geospatial PDF” technology to all users. As a result you can create “geospatial PDFs” directly from ArcGIS and other geospatial software and display them with the TerraGo Toolbar. TerraGo, however, retained the enhanced functionality of GeoPDFs, including many new additional features and enhancements.
The term “GeoPDF” refers to map and imagery products created by TerraGo software applications. GeoPDF maps and imagery use a geospatial PDF as the container for maps, imagery, and other data used to deliver an enhanced user experience in TerraGo applications. However, GeoPDF products conform to published specifications, including both the OGC best practice for PDF georegistration as well as Adobe’s proposed geospatial extensions to ISO 32000, making them consumable by applications such as Adobe Acrobat, Adobe Reader, Global Mapper, and others. GeoPDF products often include other advanced PDF features such as layers and object data that can add significant GIS functionality to the file, particularly when used with the TerraGo plugin to Adobe Reader or other TerraGo clients. TerraGo even has the capability to create navigable 3D GeoPDF models. Here is an example of a 3D GeoPDF model of the Bin Laden compound. Click to experience the interactive PDF (requires TerraGo Toolbar.)
TerraGo’s geospatial collaboration software and GeoPDF maps and imagery are a powerful solution to produce, access, update and share geospatial information and applications with anyone, anywhere. TerraGo solutions enable enterprises to extend, exchange, collaborate and exploit georeferenced maps, imagery, audio, video, forms and other intelligence in connected or offline environments. I repeat: connected or offline. This is a key GeoPDF capability that cannot be overemphasized.
I learned the hard way during numerous emergency response exercises and events that as the number of responders ramps up, local internet connectivity degrades to the point that it’s difficult to send and receive even simple emails, let alone large data sets such as imagery. GeoPDF technology permits users to collect and assemble large data sets at the early stage of an event, use them and collaborate on the GeoPDF map locally without the need to continually reload the same data from a remote server. Building on this strength, TerraGo developed numerous related products, but the company is evolving in a more fundamental way. According to TerraGo CEO Rick Cobb, the company is moving from a product-centric organization to a workflow solutions company by expanding some of its technology, providing its solutions as APIs and SDKs for integration with high-end systems and using innovative methods to bring its capabilities to remote users even in fringe, disconnected environments.
Part of this evolution included expansion of three technologies:
increased emphasis on use of locally connected mobile devices,
enhancing the capabilities of “Composer 3D” that integrates 3D data such as LiDAR point clouds with 2D data in the GeoPDF environment, and
the acquisition of GeoXray, a “big data” exploitation tool that automates the process of discovering, geospatially visualizing, monitoring and sharing relevant unstructured information from any source.
GeoXray is a web-based software application that allows users to search the Internet and social media sites for content relating to a geographic area and filtering the results by place, time and topic. TerraGo demonstrated interoperability by allowing a user to access GeoXray directly from a GeoPDF map. TerraGo’s Michael Bufkin indicated that the next step in this interoperability will be to cache the GeoXray-discovered content within the GeoPDF map when it is created, thus enabling access to the content directly from the TerraGo Toolbar. Users would then be able to discover GeoXray content even if not connected to the Internet, while using the same tools that they use for map display and collaboration.
It’s hard to fully describe the GeoPDF/GeoXray integration in this short column but picture a sample scenario which was demonstrated for me at GeoINT 2012. A hypothetical analyst needed to determine a probable location of a kidnap victim in a remote country. The analyst first used the general mapping capabilities of the GeoPDF map to identify key geographic locations. Then, using a broad array of “big data” contents such as news, blogs and social media, the analyst was able to narrow his efforts to a few key locations through the discovery and filtering capabilities of GeoXray. Combining and layering the physical geography with mapped locations of relevant GeoXray data, the analyst was able to significantly narrow sites of interest. Further viewing and local collaboration by agents in the field using mobile devices to view and collect additional data could refine the location even more.
This was quite an elegant and robust merging of GIS and “big data” in an easy-to-use application. I look forward to this tool set being a valuable addition for DOD, businesses and any agency that needs fast collaboration in complex environments both domestically and in remote locations.
TerraGo will be an exhibitor at the ESRI Federal Users Conference this week. I’m looking forward to seeing what other new developments exhibitors will be showing at the UC. Please stop me and say hello.
GPSTrackIt has released a fleet management tool called Driver, a website that enables drivers using tablet devices such as iPads and Androids to send and receive messages and plan routes.
The site provides drivers with two-way communication by chat or forms with dispatchers and fleet managers. It also provides them with route management and timekeeping utilities. Driver compliments GPSTrackIt’s recent releases for smartphones and tablet apps for dispatchers and fleet managers.
According to Eddie Bermudez, product manager, GPSTrackIt’s vehicle tracking system has been enhanced with Field Service Manager (FSM). “The FSM is the control center for all of the mobile workforce management tools available through the Driver website. Dispatchers and fleet managers use the Field Service Manager to create and send messages. The can build Quick Messages and Quick Responses that drivers can select from a list. They can also create forms with multiple questions, and multiple choice answers that streamline the communications process.”
Dispatchers enter stops and build routes using the FSM. The routes can be rearranged as new stops are added, saving drivers time and reducing the need for them to call in. The routes are pushed out to the drivers, who can then display them on maps and list out the directions. They can also use a third-party navigation app to render true turn-by-turn directions.
Soon Driver will provide route optimization. “Driver will look at all the stops and do the arranging for you,” said Bermudez. “It will evaluate the relative distances and calculate the most efficient route.”
In addition, the FSM also provides a mobile time clock. “This enables drivers to clock in and out using the tablet,” Bermudez said. “That data, as with the chats, stops, and forms, can be reported on using Fleet Manager’s reports. It provides fleet managers with a verification tool for employee timekeeping.”
One of the other advantages to using a tablet device is that it offers users a wide variety of useful mobile apps.
“Tablet devices provide our customers with a platform that not only connects them to the Fleet Manager system, but many other productivity tools,” adds John Stull, President and founder of GPSTrackIt. “Drivers can make use of other apps and peripherals to do credit card transactions, scan and transmit contracts, and perform many other important tasks.”
This subject scares me. I’m not a trained geodesist. I’m not a mathematician. Yet, I’d be derelict in my duty if I didn’t write about this subject. I know enough to be dangerous, and enough to know this subject is going to be a nightmare for people managing geospatial databases.
Headache today, nightmare tomorrow
The only reason it’s not a nightmare today is because most of you don’t know it’s even a problem. Or, you know it’s a problem, but let it slide because dealing with it is not easy. It’s going to get worse in the future, much worse. It’s going to get worse because sensors (GPS, GNSS, imagery, etc.) are getting much more accurate. The cost of acquiring high-precision (centimeter-level) data, whether it’s via GNSS, scanning or ?? is falling hard and fast. As I’ve written before, high-precision GNSS receivers are getting much cheaper. Geodata 2.0 is coming, and it is making existing geospatial databases look like my kids’ coloring books.
It reminds me of an experience I had nearly 20 years ago.
I was traveling through the southeastern U.S. demonstrating a new GPS mapping handheld that I helped develop. Mind you, this was in the early days of GPS mapping. WAAS/SBAS didn’t exist, sub-meter receivers didn’t exist, CORS didn’t exist, and real-time corrections were only a dream so almost everyone post-processed using a local base station, if they could find one — and achieving 1-3 meter accuracy was pretty dang good.
I was showing this new GPS mapping receiver to a forestry company that owned a lot of land in the southeast. We traversed a ~40 acre parcel of land, brought it back to the office and post-processed the data against a nearby GPS base station. After post-processing, the data looked very clean and I was eager to see it inserted into the company’s GIS, hoping it would slide into the right spot in the GIS and they would purchase a bunch of GPS units. No dice. When it was inserted into their GIS, the perfectly shaped polygon fit imperfectly into the GIS. It didn’t match up with the orthophotos and it didn’t match up with their existing vector data (point/line/polygon). It was offset enough to make you raise your eyebrows and think to yourself — hmmm, that’s a problem.
Of course, I did my due diligence by checking the integrity of the GPS base station data I used and verified its surveyed antenna location. Everything checked out. I was confident that my data was accurate. I started questioning the GIS manager about the horizontal datum used in their GIS database. It quickly became clear to me that the enterprise GIS database was referenced to something different than the modern horizontal datum of that era. It was also clear that there were varying types of accurate and less accurate data in the GIS. A mish-mash of geographic data with some of it based on the legacy NAD27 horizontal datum that was transformed to NAD83/86 using NADCON or something similar.
After discussing this a bit with the GIS manager, he admitted that my GPS data was likely more accurate than his GIS database, but he was clear that “I’m not going to readjust my entire GIS database for your GPS unit.” My counter-argument that “you’re going to have to do it eventually anyway” was met with “I honestly don’t see this happening anytime soon.”
I may have won the battle, but lost the war.
Later that same year, I had a similar experience in California. A major environmental consulting company wanted to delve into using GPS for mapping. I sent them one of my GPS units to try. After a few days of the company collecting GPS data and post-processing, I got the call.
“Your GPS unit isn’t accurate enough for our work.”
Whaaaat? From the outset, I was clear to them that the GPS unit would deliver accuracy within 1-3 meters, and they stated this was acceptable accuracy to them. I looked at the data. It was clean and point averages were tight. It looked very good. I verified the GPS base station they were using. No problems there.
“What are you comparing the GPS data to?” I asked.
Me: Wellllll, you know that USGS 7.5’ topos are referenced to NAD27 and have gross errors up to hundreds of feet in some places, especially rural areas, don’t you?
Him: We’ve used 7.5’ topo maps for many years and feel good about the accuracy they provide. Your GPS data is on the wrong side of the creek.
Me: Hmmm, how about you go occupy a survey mark with known coordinate and compare the GPS data to the survey mark coordinates? That will tell you how accurate the GPS is performing.
Him: We need it to work where we work, and it’s giving us data on the wrong side of the creek. Thanks for your time. Click.
Sigh, lost the battle, and lost the war.
After nearly 25 years in the GPS/GNSS and GIS industries, data mismatch (“my data doesn’t line up”) is still the #1 question I get from people.
The problem is two-fold.
People, even educated geospatial professionals, have a general lack of understanding of the different horizontal datums being used (not to mention vertical datums).
Software vendors (even the major ones), have generally done a poor job of keeping up with modern datum transformations. While most software makes it easy to transform data from one horizontal datum to another, they mostly do it wrong.
The errors can vary from a few centimeters to a few meters to tens of meters. In the world of GPS data collection, the most common datum transformation error is when software considers WGS-84 equivalent to NAD 83 and applies no transformation when, in reality, the difference between the latest version of NAD83 differs from the latest version of WGS-84 by more than a meter in most parts of the USA.
In this day of ever-increasing availability of public GIS data, it’s soooo easy to download an orthphoto (ortho-rectified aerial photograph), or GIS vector data from a public website and import it into your GIS. When importing, you’ll likely be asked to select the horizontal datum and the measurement units of the new data. More than likely, information about the new GIS data (metadata) isn’t readily obvious or available so you make your best guess from the list of choices presented. Is the data referenced to NAD83/86? Is it referenced to NAD83/HARN? Is it referenced to WGS-84? If so, which version of WGS-84? Your selection might significantly affect the accuracy of imported features of your GIS. What if you make the wrong selection with an aerial photo, then months or years later you have someone digitize (heads-up with a mouse) road centerlines, fire hydrants, manhole covers, etc., based on that aerial photo? Any transformation error you introduced when importing the original aerial photo will carry through to the digitized features.
The good news is that GIS software makes it very easy to import raster (images) and vector (points/polylines/polygons) data. That’s also the bad news. With a few clicks of a mouse, your GIS database can be infected with data you think is accurate to a certain level, but it’s really not, maybe due to the way you imported the data. I’m not saying that every piece of data imported into a GIS needs to be a certain (or the same) accuracy level. The problem is if you don’t keep track of the metadata for items that you import into your database, you will quickly lose a grip on the accuracy integrity of your GIS. As GIS data becomes more accurate, as I discussed above, the accuracy disparity among different layers in your GIS will increase. In other words, the problem will become bigger than it is today.
I’ll give you a scenario I’m involved with now that highlights this challenge. I used a pseudo-name for the company and have embellished a bit to emphasize some points, but the basic facts are correct.
ABC Company has tens of thousands of small infrastructure assets in the field across the U.S. It already has the desired location accuracy on some (within 30 cm, or 1 foot) on some of them. For the remaining assets, the company wants to improve the accuracy of the features. To do this, the company plans to use GPS/GNSS receivers to collect position and attribute information on the assets. A second requirement is to deploy GPS/GNSS receivers capable of sub-meter accuracy to navigate back to assets when necessary.
They are now in initial phase of testing various GPS/GNSS receivers.
Their first try was using a handheld GNSS receiver capable of “sub-foot” accuracy and post-processing against GPS CORS. It didn’t take long for them to figure out the workflow was a headache. I agree, the whole GPS post-processing workflow is so last decade (and mind you, I helped design one of the first Windows-based GPS post-processing software programs back in the 1990s).
For the second iteration, the workflow was much smoother. They used a GNSS receiver that utilized real-time WAAS corrections for sub-meter accuracy. The workflow was smooth due to real-time GNSS data being brought directly into ArcGIS Mobile in the field. The problem was accuracy. All of the coordinates collected during the testing were offset to the northwest by about 3 feet. Precision was great, but accuracy was unacceptable. Was it a problem with the GNSS receiver? No. When GPS/GNSS data is shifted consistently in one direction when compared to other data, it is almost always due to a difference in horizontal datums. In this case, it didn’t take long to determine that the difference was data referenced to ITRF (WAAS) vs. NAD83 (basemap). However, we had to do a little more investigation to understand which version of NAD83 was being used in order to find the best horizontal datum transformation choice in ArcGIS Mobile. It wasn’t obvious, not by a long shot. In fact, it was downright cryptic. There wasn’t a datum transformation labeled “WAAS” or anything close to it. As an example, one of the transformation names was cryptically named NAD_1983_To_WGS_1984_5. What does that mean? Which version of NAD83? Which version of WGS-1984? What does _5 mean?
With some investigation and experimenting with different transformation choices, we finally got it dialed in to a reasonable level. Remember, we were only looking for sub-meter accuracy so ~10 cm of datum transformation error here or there wasn’t significant. Even if we didn’t make the perfect transformation choice, we were close enough. However the investigation and experimenting drill was painfully time-consuming (locate a high-integrity survey mark nearby and occupy it), more than it should have been.
The next step, setting up the workflow for the “sub-foot” mapping GPS/GNSS receivers, wasn’t as easy. First of all, instead of using WAAS as a correction source (not accurate enough), they used an RTK network. The network base stations were tied to the latest version of NAD83, which is NAD83/2011. They really wanted to dial in the correct horizontal datum transformation. The challenges were a bit different than testing the datum transformation for the sub-meter equipment. They wanted to dial in the datum transformation as close as possible. Again, the datum transformation selection choices in ArcGIS Mobile were cryptic. But, this wasn’t the only challenge. Since they were using RTK GPS/GNSS receiver capable of 1-2 cm accuracy, errors within the different GIS layers emerged. Some layers were referenced to NAD83/2011, which was perfect, while other layers were referenced to much older versions of NAD83. To the software’s credit, an alarm popped up noting the difference in datums of the older layers, but didn’t give them any guidance as to how they should proceed. In that case, Cancel is the normal response and is what they selected.
After experimenting and testing the different datum transformations in ArcGIS Mobile, they found the one that seemed to produce the best results (confirmed by testing against a high-integrity survey mark). All in all, a very time-consuming process spread out over a few weeks.
A challenge that still remains is “hot-swapping” between using the RTK Network (NAD83/2011) or WAAS (ITRF08) as a source of GPS/GNSS corrections. ArcGIS Mobile doesn’t seem to deal with switching GPS/GNSS incoming datum changes very well on the fly (in the field).
If, after reading the above, you’re confused or feel the need to read it again to understand it, welcome to the club. Plenty of brainpower was spent sorting out this problem and verifying the solution. When your GIS has plenty of slop in it, no worries. When you start dissecting it at the centimeter level, you’ll really be forced to take a microscope to each data layer and all of the sudden metadata becomes very important.
This article is just an introduction to the challenge of dealing with disparate horizontal datums in your GIS. As the programmer for datum transformation at a major GIS software manufacturer said, “We are moving into a new era” in dealing with datum transformations. Although I mention Esri software in this article, other leading software vendors aren’t doing any better. I discussed the issue of supporting the 14-parameter transformation between NAD83/2011 and ITRF08 with another major software vendor late last year. Their CEO’s response? “Yeah, we just had an internal meeting on that and need to support it.” Whaaaat? I wonder how his thousands of users utilizing WAAS as a source of GPS corrections have been handling this in the past 10 years. Not surprisingly, they aren’t the only major geospatial software that is falling down in this area. More than likely the software you use isn’t handling this correctly.
Lastly, in speaking with Michael Dennis at the U.S. National Geodetic Survey, he said that while the 14-parameter transformation algorithm is important, the step that people are leaving out is reconciling epoch dates of the data. Why is a date stamp of the data important? That’s the focus of my next article on this subject.
A new technology to combat dozing off when driving is being developed by two universities with industry partner Ficosa. The drowsiness alerter, Somnoalert, is a smartphone application that uses inertial sensors and GPS data to detect movements that are characteristic of nodding off at the wheel, such as deviation from the driving lane or sudden corrections. A later prototype also incorporates biomedical sensors to analyze respiration data.
“One of the main causes of car accidents is drowsiness, especially on long highway trips,” explains Santiago. “Most monitoring systems developed in the last few years have been integrated systems that need to be connected to the car’s system. Our device combines our group’s expertise in sensors and biological data analysis with FICOSA’s vehicle know-how, and is completely portable.”
“Accidents related with drowsiness have a very high social and economical impact, that the key automotive industry players are facing as a whole, in order to reduce current accident statistics,” said Alan Montesi, who heads the project for FICOSA.
Pole Star has launched its new indoor location platform, NAO Cloud. NAO Cloud simplifies the deployment of indoor location solutions by introducing an automated deployment process that dramatically reduces time-to-market and the costs of indoor location-based services, Pole Star said.
NAO Cloud integrates the NAO Campus Software Deployment Kit (SDK), and enables customers and partners to deploy NAO Campus, Pole Star’s indoor location solution, in just a few hours, by using cloud-based software tools as well as positioning databases already available and shared by worldwide partner program members.
In addition, third parties will have access to NAO Cloud’s crowdsourcing capabilities, eliminating field interventions for a simpler, faster and more affordable deployment and maintenance process, Pole Star said. Behavioral analytics or geofencing are also supported by NAO Cloud to maximize the monetization of value added location-based services.
The NAO Cloud platform targets a wide range of businesses such as venue owners, advertising platform providers, application developers, global solution integrators or network operators. NAO Cloud makes deployment, integration in mobile apps and maintenance of indoor positioning services a simple process, from a single venue to a worldwide multi-site coverage, Pole Star said.
“The indoor location services market has reached maturity. Multi-venue owners, marketing agencies and major telcos understand the challenges and the value of hyper-local information and real-time interactions with customers and related Indoor Location Analytics. Indoor positioning is the core technology that brings high value,” said Christian Carle, CEO of Pole Star. “NAO Cloud is the result of years of innovation and deep market experience through very large and complex field deployments around the world.”
In 2012, Pole Star achieved several major innovation milestones, such as the integration of Bluetooth Low Energy and Inertial Sensors in its NAO Campus fusion engine, in addition to Map Data, Wi-Fi and GPS signals. The dynamic combination of these technologies provides today the best indoor location performance results in the market, while addressing any type of building and minimizing network infrastructure, deployment and maintenance costs, Pole Star said.
The NAO Campus solution is now available for more than 80% of the smartphone market, compliant with Android and iPhone devices and embedded in consumer applications on the Google Play Marketplace and the Apple App Store.
Today, Pole Star’s indoor location solutions have been deployed in more than 43 million square feet, in 15 countries such as airports (Paris Charles de Gaulle), shopping centers in Europe and North-America, museums and department stores. In 2011, Pole Star opened its North American headquarters in Palo Alto and has expanded its international presence in 2012, building deep partnerships with companies in Europe, North-America, Asia, Australia and the Middle East. Finally, at the end of 2012, in time for the holiday season, Pole Star launched, its “living lab” mobile application, Mall Buddy, that covers 9 of the biggest malls in Silicon Valley, from San Francisco to San Jose and demonstrates the worldwide extension capability of Indoor location services.
Swedish-based Handheld, maker of mobile computers designed for extreme environments, has integrated u-blox’ GPS modules in four of its most popular products: the Algiz 7 and Algiz 10X tablets, Algiz XRW notebook, and Nautiz X1 smartphone. These tough computers are designed for and used in demanding environments such as polar expeditions, marine exploration, and rescue operations, as well as outdoor industrial applications such as utility maintenance and logistics. The devices depend on u‑blox’ LEA, NEO, and AMY families of compact, high-performance GPS modules to provide reliable navigation and positioning in challenging conditions.
“Handheld is proud to have achieved an industry-leading position for dependable, ruggedized mobile computers that can be trusted to work in the most hostile environments” said Jerker Hellström, CEO Handheld Group, “To achieve this extremely high-level of performance, we only select components with the highest reliability on the market. GPS positioning is one of the most important functionalities of our products. For this mission-critical feature, we chose u-blox.”
Handheld’s lineup of rugged PDAs and mobile computers is specifically developed for use in tough environments in industries such as geomatics, logistics, forestry, public transportation, construction, mining, field service, utilities, maintenance, public safety, military and security.
FICOSA demonstrated a telematic unit integrating a multi-service antenna module for positioning and satellite navigation supporting all the geographic positioning standards at the 2013 Mobile World Congress in Barcelona in February.
The major advantage of this technological innovation is that the proposed multi-channel GPS/Galileo/GLONASS/BeiDou/QZSS receiver allows tracking across all the different navigation and positioning satellite standards worldwide, so that automakers can the having to manage different variants of in-vehicle telematic units (iVTUs) depending on the geographical market. iVTUs are needed for emergency call function, fleet management, and other functions. It incorporates an OriginGPS antenna.
The new module is a complete system-in-package featuring miniature surface mount device technology footprint designed to commit unique integration features for high volume, low power and cost-sensitive applications.
In addition, the reduced size of the receiver module makes the most of a stacked-up in board integration through miniaturized integrated circuits and surface mount devices, allows an aggressive reduction of the iVTU packaging, which is advantageous for the OEM for car assembly, iVTU localization inside the vehicle, and weight reduction.
The innovation represents the result of the international collaboration between FICOSA and OriginGPS. “We view the telematics market as a growing market and it is our privilege to cooperate and partner with Ficosa and its excellent engineering team,” said Haim Goldberger, CEO and founder of OriginGPS.
“In FICOSA, innovation and technology are two main tools for our future and working with OriginGPS is a great issue,” said Jose María Forcadell, Advanced Communications Business Unit Director at FICOSA.
GT-1 asset tracker combines GPS, RFID, and Bluetooth technologies. Photo: Geoforce
Geoforce, Inc. is announcing commercial availability of its GT-1 asset tracking device that can track field equipment in locations and conditions previously too challenging for other devices to function effectively. A globally certified GPS device, the GT-1 enables oil and gas service providers to proactively monitor and share data on vehicles and equipment for more cost effective operations, helping to meet ongoing environmental responsibilities, the company said.
“We have been waiting a long time for a device like this,” said Michael Rolston, operations manager at Permian Equipment Rentals. “It’s small, it’s incredibly rugged, it will last years without replacement. It’s also surprisingly low cost — given all its features and capabilities.”
The GT-1 was previously offered in limited release to several major international service and rental companies beginning in the fourth quarter of 2012. To date, thousands have shipped and are actively tracking oilfield assets around the globe.
By Zainab Syed, Jacques Georgy, Abdelrahman Ali, Hsiu-Wen Chang, and Chris Goodall
Using a select set of components, a navigation software development kit can easily be configured to fit a variety of mobile and portable devices. Testing on several current devices demonstrates that the kit’s use of sensors already present in smartphones to enable entertainment can provide 3D positioning when satellite signals are degraded or absent, such as in urban canyons or in deep indoor environments. The solution also provides the heading of the user, the 3D orientation of the device, and the user’s velocity, without restriction on device usage.
Location-based services (LBS) have evolved to the point that a smartphone is considered incomplete if it does not have navigation functionality. In fact, basic navigation functionalities are no longer sufficient, because of the limited capabilities of traditional solutions. Traditional navigation techniques are usually based on the trilateration of GPS signals. Smartphones use Assisted GPS (AGPS) technology, which utilizes pre-knowledge about the satellite constellation to provide GPS-based positions in urban canyons and indoor environments, a capability once considered impossible. Because GPS signals cannot reach indoor environments, some companies have developed map databases to provide a positioning solution using available Wi-Fi signals. The concept is simple: to provide absolute positioning where GPS signals are too weak or are unavailable. However, such a solution requires continuous updates of ever-changing Wi-Fi hotspot maps, making this a costly system to manage. Nevertheless, it is an attractive option for positioning in the absence of GPS signals.
Because LBS demand reliability, continuity, and accuracy in all environments, as well as information about the headings of the device and user, many research groups and technology companies are working to achieve these goals by integrating the aforementioned positioning methods with pre-existing sensors in smartphones. Currently, micro-electro-mechanical systems (MEMS) sensors are used predominantly for entertainment applications in the phone. The orientation of the screen is sensed by the MEMS accelerometers, which switch the display orientation according to the user’s needs. Some applications use the accelerometers and magnetometer to provide an indoor navigation solution starting from a user-defined position, but only if the smartphone is kept in a fixed orientation — an unrealistic assumption. Other recent research works also include gyroscopes for navigation. In general, it has been found that embedded mobile-phone sensors are insufficient for reliable navigation purposes because of very high noise, large random drift rates, and also because it can be assumed that the mobile device is able to freely change orientation with respect to the moving platform (the human body while walking, or a vehicle while driving).
This article provides the results of using an efficient and high-rate navigation platform with low computational requirements for mobile devices. Known as the Trusted Portable Navigator (T-PN), it utilizes a smartphone’s existing MEMS sensors. Despite some of the challenges with MEMS, the T-PN can provide a real-time, continuous, and reliable navigation solution that works regardless of the motion pattern of the user. Example motion patterns include walking with the smartphone indoors or outdoors; driving in clear sky conditions, downtown, or through tunnels and underground parkades; or a combination of walking and driving in any environment.
The main challenge with low-cost MEMS sensors in smartphones is that they cannot be used without proper error modeling because of high noise characteristics and bias instabilities. Thus, the T-PN has innovative algorithms that autonomously develop custom error models, turning the available sensors into navigation-capable inertial sensors, without any restrictions on the user or any delay in the navigation solution.
Current consumer mobile devices can be used in a variety of ways; for example, while texting, on the ear, in pocket, dangling freely while handheld, and on a belt. The orientation of the phone changes significantly with each use case, which makes accurate sensor-based navigation very difficult to achieve if referenced to the user. The common practice in traditional inertial navigation is to attach and align the device to the moving body. However, it is unrealistic to ask a user to keep their phone in any specific orientation. To solve this problem, the T-PN calculates these orientation angles in real-time and uses them as corrections for the user’s attitude and position.
The ultimate demonstration of the T-PN’s capabilities is its real-time performance in smartphones and tablets. The tests described here were performed on the commercially-available Android and QNX operating systems in tablets and smartphones. The T-PN was packaged and built at the native level to ensure computational efficiency. Several devices were used in the real time testing, including: the Samsung Galaxy Nexus, the Samsung Galaxy Note, the Samsung Galaxy S III, and the Blackberry Playbook. This device selection is an accurate sampling of the current mobile technologies available today.
Other manufacturers will have more of these devices running newer versions of Android and other operating systems. All of these devices include tri-axial gyroscopes, tri-axial accelerometers, tri-axial magnetometers, a barometer, and a GPS chipset with AGPS capabilities. All the devices used feature different brands of these low-cost sensors.
Sensor Calibration
The sensors need to be calibrated for two different types of errors to ensure a precise and accurate navigation solution. The first type of calibration is known as deterministic errors calibration, which includes the estimation of initial turn-on biases and scale factors of the sensors. For very high-cost systems these errors are usually negligible, but mobile phone-grade sensors show high variations from turn-on to turn-on.
The second type of calibration is more involved and labor-intensive, as it requires large static datasets. Allan variance curves are calculated to estimate the bias instability and random walk parameters. These parameters are called stochastic error model parameters and are necessary to obtain optimum results for longer periods of standalone navigation. They are also very important when attempting to design a consistent filter. For very low-cost sensors, these parameters may change from unit to sensor, and over time for the same sensor. This means that individual systems may demonstrate different performances with the exact same integration software.
The T-PN eliminates the need of any calibration, as it uses a patent-pending technique that automatically completes all the required calibration within 5–10 minutes of the navigation mission. The only requirement is the availability of a good GPS position, velocity, and timing (PVT) solution for at least 5–10 minutes. Starting from generic calibration parameters, artificial intelligence techniques quickly narrow down the search to the most optimum error-model parameters. This makes the T-PN suitable for navigation use with mobile phone-grade inertial sensors.
Changing Orientations
Changing orientations cannot be avoided for smartphone-based navigation. While navigating, users will take calls, text, and check their position; therefore it is impractical to request that the user keep the phone fixed to their body. The solution must be robust to provide navigation for these common use-case scenarios.
The T-PN uses patent-pending techniques to identify the changing orientations as they occur and adjust the user’s navigation solution accordingly. The result is a seamless and robust solution, with or without GPS.
Mode of Transit
Mobile phone navigation cannot be restricted to pedestrian-only or vehicle-only cases. The user will be carrying the device wherever they will go, which requires the navigation software to be adaptable for the user’s mode of transit.
Through a patent-pending technology, the user’s mode of transit is detected. Different modes may include walking, using the stairs, driving, riding an elevator, and static periods related to the above modes. Once the mode is detected, the appropriate algorithms and constraints are applied to ensure minimal navigation drift, even for long periods of standalone sensor navigation. There is no restriction on modes of transit or any requirement to perform a special task, making the T-PN user-friendly and efficient.
T-PN Overview
The T-PN is highly customizable software that converts any quality and grade of inertial sensors into a navigation-capable system. In other words, it can be used on any available smartphone operating system, such as Android. This navigation engine takes any available measurements and improves the navigation results by filtering the updates. GPS is the most common type of external update that provides absolute position and velocity information to the inertial engine and reduces time-related errors.
Wi-Fi is another absolute update for positioning in deep indoor scenarios, and is also accepted by the T-PN. Wi-Fi measurements are noisy, but the T-PN integrated solution smooths the noise and closely represents the user’s actual position. Wi-Fi updates are optional for T-PN, but they will enhance the solution if long periods of indoor navigation are desired.
Physical movements of the user, such as pedestrian dead reckoning, zero-velocity updates, and non-holonomic conditions are used as constraints to improve the navigation solution.
The constraints are also tailored to the user’s mode of transit to ensure the most robust solution for the user. Mode of transit is automatically detected on a continuous basis.
If additional sensors such as magnetometers and barometers are present and properly calibrated by the T-PN software, their readings can be used as optional updates. Figure 1 shows a complete flowchart of the algorithm for the T-PN. The dashed lines show the optional updates for the T-PN.
Figure 1. The T-PN algorithm flowchart.
Hardware Description and Use Cases
The test platforms used are smartphones and tablets running different versions of Android and QNX. The opening picture shows some of these units, listed here with their operating systems.
A variety of use cases, listed in Table 1, are currently supported in the T-PN.
Table 1. Current supported use cases.
Results
The results are divided into three sections:
the results for consumer navigation and their respective LBS applications;
tracking applications for personnel on-foot and in-vehicle;
and driving with or without GPS with the device left on the seat or holder with or without a connection to the on-board diagnostic system (OBDII) of the vehicle.
Consumer Navigation, LBS App. This is a very typical use case. It involves the user starting the navigation after parking his/her vehicle to locate a certain destination in an indoor environment; for example, a specific store in a shopping center or an office inside a building. As the user heads deep indoors, GPS will stop providing any useful positioning information, as illustrated in Figure 2 (blue line). The user started the navigation in texting portrait mode, then held the phone in hand for some time and let it dangle naturally, and then finally puts the phone in his or her pocket. The trajectory in red is the T-PN solution and the blue line shows the available GPS solution. The Samsung Galaxy S III was used in this trajectory, with a maximum error of less than 7 meters for 2 minutes of deep indoor navigation.
Figure 2. GPS positioning solution in blue is given with T-PN solution in red for a typical outdoor/indoor environment using Samsung Galaxy S III.
Figure 3 shows a trajectory collected and processed on an S III with GPS signals (including multipath) in blue provided with the T-PN solution in red. During the navigation, the user was making a phone call with the phone on the ear. The maximum error stayed within 17 meters for 5 minutes of indoor navigation with severe multipath in GPS signals. It has to be noted that the heading solution would have converged better if the user walked outdoor for an adequate time, but here the user went straight indoors a few seconds after starting.
Figure 3. GPS positioning solution in blue is given with T-PN solution in red for a typical indoor environment with multipathed GPS signals using T-PN on a Samsung Galaxy S III.
The trajectory in Figure 4 was collected and processed on a Samsung Galaxy Note. The user was holding the Note in texting portrait mode in Shanghai’s downtown core. When the user entered the building, GPS positioning information became unavailable, and the only positioning information available was from T-PN (as shown by the red line in Figure 4). The maximum error after approximately 2 minutes of indoor trajectory was less than 6m.
Figure 4. Trajectory collected and processed on a Samsung Galaxy Note in downtown Shanghai China. Red line is the T-PN solution while the blue is GPS solution.
Figure 5 shows a pure indoor trajectory without GPS, collected and processed on a Samsung Galaxy Nexus. The user walked in a loop for 4 minutes and then returned back to the same location. The maximum error stayed within 13 meters, even with the phone changing orientation with respect to the user. This trajectory was collected at Computex 2012 conference in Taipei.
Figure 5. Pure indoor trajectory collected and processed on a Samsung Galaxy Nexus phone with different user orientation of the phone.
Tracking Applications. Another usage of T-PN can be related to tracking of personnel such as firefighters. In this case, the tracking device will be attached to the users for a high-accuracy solution. To show the performance, a Samsung Galaxy Nexus was tethered to the user in a chest mount strap. The user took a trajectory that started outdoors and then went indoors for over 9 minutes, covering multiple floors and taking elevators and stairs to access the different floors. At the end of the trajectory, the error was less than 6 meters, or 1.5 percent of the distance traveled. Figure 6 shows the results, with the red line showing the T-PN solution and the blue line showing the GPS solution.
Figure 6. Samsung Galaxy Nexus running T-PN in real time for tracking application.
Figure 7 shows the result of the tethered chest-mount system that was connected wirelessly with a vehicle’s OBDII while inside that vehicle. The vehicle entered an underground parkade with no GPS availability and completed two full loops inside the parkade before exiting.
Figure 7. Samsung Galaxy S III running T-PN in real time for tracking application of the personnel inside a vehicle with OBDII.
Consumer Vehicle Navigation. The results of using the T-PN platform on a Blackberry Playbook in real time in the downtown Toronto Eaton Centre parkade appear in Figure 8. The Playbook was left untethered on a seat during the navigation. The T-PN was able to bridge the complete loss of GPS signals (blue line) in the multi-level parkade, and to effectively filter the multipath in the GPS signals in the Toronto downtown core.
Figure 8. T-PN platform running on a Blackberry Playbook in red is provided against the GPS solution in blue.
The next set of results are for a changing misalignment case within the trajectory. In this case, T-PN was running on a Samsung Galaxy S III and evaluated in Calgary’s downtown core. The GPS signals were erroneous due to multipath (as shown by the blue lines in Figure 9), while the T-PN solution was able to provide a proper trajectory, including an almost perfect figure-eight.
For the final sets of results, a Samsung Galaxy S III was placed (untethered) on a seat in a vehicle with a wireless connection to the vehicle’s OBDII. Despite the misalignment, the T-PN showed the three loops in the parkade almost perfectly, as shown in Figure 10.
Figure 9. Downtown Calgary trajectory collected and processed on a Samsung Galaxy S III with changing misalignments in a gooseneck cradle. T-PN solution is in red and the GPS is provided in blue.Figure 10. Underground parkade trajectory with wireless OBDII connection on a Samsung Galaxy S III running T-PN software. T-PN solution is in red and the GPS is provided in blue.
Conclusion
Today, mobile phones are used as navigation devices. GPS often fails to provide an accurate positioning solution in urban canyons and deep indoor environments because GPS is either not available in these environments or will provide erroneous positions because of multipath.
The T-PN provides accurate positioning everywhere by converting the pre-existing inertial sensors of mobile devices (such as tablets and smartphones) into navigators. The results were provided for walking and driving cases where GPS positioning information was unreliable or unavailable. In all these cases, the T-PN solution was able to successfully provide enhanced navigation solution of the user.
Acknowledgment
This article is based on a paper first presented at ION GNSS 2012, September 2012, Nashville, Tennessee.
Zainab Syed is a co-founder/VP engineering at Trusted Positioning Inc. She obtained her Ph.D. from the University of Calgary. She has 6 patents pending and more than 50 publications on integrated navigation systems.
Jacques Georgy is the VP of R&D and a co-founder of Trusted Positioning Inc. He received his Ph.D. in electrical and computer engineering from Queen’s University, Canada. He has 10 filed patents, written a book, and more than 40 papers.
Abdelrahman Ali is an algorithms designer at Trusted Positioning Inc. He is also a member of the Mobile Multi-Sensor Systems Research Group at the Department of Geomatics Engineering in University of Calgary where he is completing his Ph.D.
Hsiu-Wen Chang is an algorithms designer at Trusted Positioning Inc. She is also a member of the Mobile Multi-Sensor Systems Research Group at the Department of Geomatics Engineering in University of Calgary where she is completing her Ph.D.
Chris Goodall is the CEO/co-founder of Trusted Positioning Inc. Chris has been working in developing, deploying, and evangelizing multi-sensor navigation systems for more than 8 years. He has more than 40 publications and seven patent applications.
Every time I see a headline or read an article concerning BYOD (Bring Your Own Device) from a government source, where that source details only the risk associated with BYOD, especially where GPS/PNT (position, navigation and timing) is concerned, I am incredulous. Consider these recent BYOD headlines:
BYOD – Disaster Waiting for Government Networks
BYOD – Bring Your Own Disaster to the Government Enterprise
BYOD – Are the Military Networks Ready?
BYOD – Bring Your Own Destruction
BYOD – A Huge Security Risk?
BYOD – A Smart Choice or a Cyber Disaster?
Historical Perspective
The naiveté of the authors that penned these stories astounds me, as frankly they are out of step with the times by about 2,000 years. BYOD and the military go hand in hand. During Roman times, except for conscripts or slaves, Roman soldiers were expected to furnish their own supplies, their own weapons, their own horses and their own support. Often they brought their own slaves/servants to care for them in camp. In our (U.S.) Revolutionary War, many of the ragtag regiments were formed from state volunteers and local militias who were commanded by officers who, having paid for their commissions, supported the soldiers they brought to the fight, with food and uniforms; many were even expected to bring their own weapons and ammunition. The same applies to our (U.S.) Civil War, the War Between the States or the War of Northern Aggression, as my Southern colleagues are wont to constantly remind me.
Since warfare began, warfighters have supplied their own equipment (BYOD), and today’s warfighters are no different, especially when it comes to personal electronic equipment, even though antiquated DoD (Department of Defense) regulations frown on such behavior. Hopefully you can see where I am going with this, especially as it relates to GPS/PNT user equipment. Unfortunately, DoD regulations also specify our warfighters in all services must utilize the government-supplied GPS equipment known as MUE (Military User Equipment), and even specifies the consequences of not adhering to that inane policy. Consequently, warfighters generally have the GPS MUE readily available if it is embedded, thereby avoiding the horrendous user interface, but they invariably also have their own personal GPS/PNT devices close at hand.
These BYO-GPS-D are, without a doubt, more useful, certainly more user friendly, and actually provide a modicum of situational awareness, with such incredible features as actual moving color maps, annotated roads and rivers, up-to-date geographical features and even voice guided navigation — all features not available on the GPS MUE as a stand-alone unit today. Some PNT devices answer verbal inquiries from their users. Can you say, “Hi Siri, where am I?”
Fast Forward: First Gulf War
Consider the first Gulf War in 1990, which in GPS lore is touted in military aviation circles as the turning point for GPS transitioning from just another en route navigation system to a weapons systems multiplier and situational awareness tool that made believers of even the most jaded fighter pilots and land warriors. Suddenly fighter pilots and weapons systems operators were scoring “shacks” or direct hits on targets, on every sortie. Instead of using four bombs to hit one target, four bombs now equaled a direct hit on four targets — a phenomenal increase in accuracy, with minimal collateral damage, all due to the Global Positioning System.
For land warriors, the famous “left hook” strategy, employed during the midst of a major, once-a-decade sandstorm that placed American warriors behind the Iraqi forces occupying Kuwait, was widely credited with bringing the ground war to a close in just four days, and it could never have been accomplished without GPS. However, the part of this story that often gets misinterpreted is the sudden appearance of BYOD GPS devices during that extremely short duration conflict (August 2, 1990, until February 28, 1991).
Newspapers and military magazines carried numerous pictures of commercial/civil GPS devices taped to military vehicle windshields, windows on ships bridges, in fighter cockpits, inside tanks and fighting vehicles — and of course carried by individual warfighters, despite regulations to the contrary.
I Don’t Know Where It Came From Sir…It Just Magically Appeared!
What we tend to overlook is that these BYOD or personal PNT devices, despite warfighter protestations to the contrary (“Methinks thou dost protest too much…”) did not just appear overnight. Warfighters carried them in flight-suit pockets and briefcases for years. They saw minimal use, and then the U.S. decided to fight a war on and over a featureless desert. And I can confirm first-hand that navigating over a featureless desert without any external navigation aids is particularly troublesome. No landmarks, no ground-based navigation aids, no radar returns, and frequent sandstorms that obscure everything in sight and radically change the landscape make life a real challenge for warfighters prosecuting a war. Navigation in this environment is challenging at the best of times; add the fog of war and it becomes a nightmare. General William Tecumseh Sherman said “War is hell!” and while it can certainly never be a walk in the park, add GPS and precise navigation along with precision targeting/bombing becomes infinitely doable.
Personal Experience
I sat in my first aircraft cockpit and took my first flight more than 50 years ago. Contrary to popular belief, neither Orville or Wilbur Wright were my first flight instructors, just close friends, but I did learn a great deal from Charles Lindberg. Seriously, I can tell you that in the “good ol’ days” an inordinate amount of airborne time was spent determining your position/location, airspeed, altitude and heading to your destination or next waypoint, often with wildly varying degrees of accuracy. Ask any aviator hailing from that era and they will tell you we really had to work at it. It was a constant struggle where IFR (Instrument Flight Rules) frequently equated to “I Fly Roads.” Certainly it was gratifying when it all worked out, but also extremely frustrating when it did not, and there was no alternative.
Nature of the Beast
Fighter pilots by nature tend to be vain and querulous creatures that by definition are the best at what they do. Did you ever meet one who wasn’t? Just ask them and they will be quick to tell you they are the best fighter pilot in the world, every one of them. And they hate to ask for directions or admit they are lost, male and female alike, hence the old adage, “You can always tell a fighter pilot, but you can’t tell them much.” Indeed, just ask any fighter pilot worth their wings and they will invoke the Daniel Boone response when asked about being lost. When asked if he had ever been lost, that great woodsman, statesman and explorer replied, “Lost? No I can truly say that I have never been lost… I was mighty bewildered once for about four days, but never lost.”
Unfortunately pilots and/or navigators don’t have the luxury of pulling over and checking for moss on the south side of trees. But one glance at a GPS device in flight (it does not have to be an aviation-grade receiver) will tell you your current heading, time and desired heading to your next waypoint and final destination, speed along the ground, altitude, and of course current position down to a meter or better. This wonderful device leaves the intrepid aviator with time to concentrate on putting weapons on target, which, if they are also GPS guided, is almost a cinch.
Now you understand why aviators were among the first warfighters to embrace BYO-GPS, and why they seemed to just “pop-up” during the first Gulf war. Today’s ultra modern jets, such as the F-22 and F-35, have built-in GPS/PNT systems with redundant inertial systems, Doppler systems, and of course radars that are all tightly integrated. Some smart weapons even have their own GPS and laser systems on board. But you can bet your next paycheck there is a backup civil/commercial battery-operated BYO-GPS in a flight-suit pocket or helmet bag, just in case, as fighter pilots also have a great sense of self-preservation.
Warfighter GPS Equipment Database
I have personally compiled a “Warfighter GPS Equipment Database” over the last 10 years, since we have been at war in Iraq and Afghanistan. The database is comprised of more than 8000 entries from warfighters from all services, U.S. and allies alike. Only 1 in 40 warfighters utilize issued GPS MUE as a stand-alone handheld device, but every single warfighter (that’s 100 percent, a rare event in statistics) in the database proudly possessed and freely spoke about their own personal BYO-GPS device, with the majority of them being various iterations of a Garmin device, with Trimble devices and iPhones coming in a close second and third; although the iPhone and other smartphones are rapidly gaining ground on all the PNT devices in theater. So the bottom line is when it comes to BYOD, GPS is alive and well and has been for the last 23+ years with no end in sight.
BYOD Here to Stay
While thousands of warfighters have written me to say, “I love my Garmin, Trimble, iPhone, etc.” I do not have a single letter or email saying I love my PLGR or DAGR (precision lightweight and defense advanced GPS receivers or MUE). However, I will and must caveat my BYOD position by stating, as I always do, that while the PLGR and DAGR are, in my opinion, woefully inadequate as handheld PNT devices, they are extremely functional and sometimes the best/only option warfighters currently have as an embedded device, especially in a GPS-denied environment. Anything that improves on the display, battery life and user interface of the current GPS MUE is to be applauded.
So to be clear, I would never advise a warfighter not to utilize the GPS MUE issued to them, but would certainly encourage them to have a backup or two. Fortunately that encouragement is totally superfluous as I have yet to meet a warfighter who did not have at least one civil/commercial PNT receiver as a backup, even in the cockpit. During a recent visit to a local firefighting C-130 squadron, the navigator utilized the on-board, original equipment MAGR GPS unit, a Trimble unit, Velcroed to the navigation console, and two laptops with different independent GPS capabilities, such as color real-time moving map displays, and the navigator had a BYOD Garmin in his flight suit pocket. QED!
BYOD is here to stay!
Let’s embrace the technology of the 21st century, stop asking if our warfighters, government employees and government contractors should be allowed to use their own PNT, computer and communication devices, and begin incorporating the smartest and best devices in the world into our networks and enterprise infrastructures. For all the hype to the contrary, there really is no alternative.
Until next time, don’t forget to BYOD and happy navigating!
Trimble announced today functionality updates to its integrated GNSS survey receiver portfolio, which includes the Trimble R4, Trimble R6, Trimble R8 GNSS systems and is rounded out by the recently released Trimble R10 GNSS System (pictured at right).
The updates include increased satellite tracking and real-time kinematic (RTK) performance. These improvements modernize the integrated receiver portfolio to add functionality, flexibility and capability as well as more options for surveyors, Trimble said.
“With the introduction of the next-generation Trimble R10 GNSS system, we felt it was an ideal opportunity to modernize the complete integrated receiver portfolio,” said Erik Arvesen, vice president of Trimble’s Survey Division. “The additional functionality in the Trimble R4, R6 and R8 provide surveyors with more capability, flexibility and additional receiver options to meet their ever-changing business needs.”
Trimble R8 GNSS System. The Trimble R8 includes integrated Trimble Maxwell 6 ASICs offering 440 channels. Powered by Trimble 360 technology, the Trimble R8 provides consistent and reliable tracking of signals for all existing GNSS constellations and augmentation systems, including GPS, GLONASS, Galileo, BeiDou and QZSS. Using the Trimble R8, surveyors can connect directly to the controller, receive RTK network corrections and access the Internet via comprehensive communication options.
Trimble R6 GNSS System. Featuring Trimble R-Track satellite tracking technology, a Trimble Maxwell 6 ASIC with 220 channels and support for all GPS and QZSS signals with GNSS upgrade options, the Trimble R6 provides surveyors with a completely scalable and flexible solution. The Trimble R6 supports GPS L1, L2, L2C, and L5 signals and QZSS as standard and offers upgrade options to support GLONASS, Galileo and BeiDou signals. The Trimble R6 delivers the accuracy and reliability required for precision surveying with superior tracking and RTK performance.
Trimble R4 GNSS System. Designed for use with the new Trimble Slate Controller and Trimble Access field software, the Trimble R4 GNSS System provides a dedicated and reliable GNSS solution that is effective for both real-time and post-processed GNSS surveys. The Trimble R4 now supports GPS L1, L2, and L2C and QZSS signals as standard and also offers GLONASS, Galileo and BeiDou support upgrade options. The system includes Trimble R-Track technology and a Trimble Maxwell 6 ASIC with 220 channels.
Trimble R10 GNSS System. The Trimble R10 GNSS system is the premier solution of the integrated survey receiver portfolio. Designed to increase productivity, the Trimble R10 provides powerful functionality, including Trimble 360 receiver technology, precise position capture with Trimble SurePoint technology, the cutting-edge Trimble HD-GNSS processing engine and Trimble xFill bridging technology to “fill in” for RTK corrections in the event of temporary radio or Internet connection outages.
The updated configurations of the Trimble R4, R6 and R8 as well as the Trimble R10 GNSS system are available now through Trimble’s Survey Distribution Channel.
