Xplorer V8 is available as a white label application or as client-side libraries depending on the degree of customization required. deCarta’s L2 advanced local search technology is fully integrated into the platform to help users find destination addresses or local points of interest (POI).

deCarta navigation technology powers products such as GM OnStar, Ford Sync, INRIX, Appello, TCS and MotionX GPS Drive. With Xplorer V8, deCarta lets developers tightly integrate that functionality into their own applications or to build custom navigation applications. Examples of the use of Xplorer V8 include:

• Local search applications that offer route guidance to the search destination from a mobile phone or tablet.
• Branded navigation applications for global automotive companies.
• Mobile applications that display places of interest in a vector map display with smooth panning, rotation and zooming.
• Fleet management solutions that offer route guidance and tracking to ensure that drivers are directed efficiently to their destinations.

deCarta has already engaged with customers in each of these areas and expects to be announcing new partners for Xplorer V8 in the coming months.

The Xplorer V8 platform consists of a cloud-based service and a set of core client-side libraries that work together to provide a high-quality navigation experience.

The Xplorer V8 Navigation Cloud Services provide local search and navigation response based on deCarta’s geospatial technologies. deCarta hosts these services in global data centers in Santa Clara, London, Seoul, Beijing and Sydney.

The Xplorer V8 Core Libraries are integrated into client side applications.  They support three critical functions that can be used together as a group or individually as needed by the customer.

• Local Search:  Single line search and geocoding based on deCarta’s L2 technology.
• Guidance and Routing: Voice guided navigation, displayable as an overview, a list of directions or in turn-by-turn sequence.
• Map Display:  Vector-based maps that support turn-by-turn navigation, voice guidance, 3D display, immediate off route determination and rerouting.

Xplorer V8 libraries are compatible with all Android-based platforms for mobile devices, tablets and automotive embedded systems.  Apple iOS versions will be available at the end of June.

For companies interested in a turn-key navigation solution, Xplorer V8 is also available as a white-label navigation application that can be branded to match the customer’s needs.

“Industrial-grade navigation engines are extremely hard to develop. To meet the demanding consumer expectations, they have to perform well, with speed and accuracy across a wide range of circumstances,” said J. Kim Fennell, CEO of deCarta. “Xplorer V8 packages all of deCarta’s navigation experience and makes it available for application developers to integrate directly into their apps.”

Xplorer V8 is available immediately for deployment in North America and Australia, with Western Europe coverage coming in June.  Other countries will be included in the following months.

The number of cyclists in London is on the rise, along with safety risks that arise when trucks and cyclists both are traversing busy London junctions and interchanges.

The ProNav HGV Cyclist Alert software was developed in association with Transport for London (TfL) to provide a commercial vehicle driver with an audible and visual alert as he or she approaches a junction (or section of road) that has been determined to be a location where there are  high volumes of HGVs and cyclists. A warning symbol is displayed on the navigation system’s mapping that projects a 50-meter radius “warning zone” around each HGV/Cyclist convergence area. Drivers are also provided with a short audible tone as a reminder, giving the driver plenty of time to check for any cyclists on the road, Navevo said.

The HGV Cyclist Alert software uses data provided by TfL and the up-to-date Department for Transport HGV and pedal cycle flow figures for London’s road network. The dataset uses this information to identify locations where large numbers of HGVs and cyclists converge. Initially, 100 high-convergence areas across London have been included (alerts at every junction would be counterproductive to drivers). Working with other local authorities both in London and nationally, Navevo plans to increase the level of coverage and will provide free updates when new data becomes available.

“A navigation system is something a driver is likely to be listening to as they approach a junction, and so it makes perfect sense to also alert the driver of the risk of cyclists, reminding them to be observant and drive safely,” says Navevo CEO, Nick Caesari. “The safety of drivers, cyclists and other users of the road is a concern for everybody, and we are proud to lead the navigation industry by launching this ‘world first’ safety feature, which we believe could significantly contribute in improving road safety and reducing the number of incidents involving HGVs and cyclists.”

“For many years, London has worked to lead the way in pushing for the adoption of safer lorries and safer lorry driving,”
Ian Wainwright, head of Freight and Fleet at Transport for London. “The creation of a specific cyclist alert for HGV drivers is another positive step forward and will help to further raise awareness and improve cycle safety across the capital.”

EverNav was initially released on September 2012 as an HTML5 turn-by-turn solution aimed at application developers and mobile marketing campaigns. The solution was quickly adapted by a large number of customers. Now, as part of the preparation for Mozilla’s release of the Firefox OS, a dedicated EverNav version will be released in April.

“We were one of the first few companies to release HTML5 based navigation back in 2012 and we are proud to be the first to release a dedicated navigation solution for the Firefox OS today. HTML5 technology has come to the point that we were able to develop a full-featured navigation user experience, and we are extremely happy with the result,” said Ziv Avni, AtlasCT’s marketing director.

EverNav for Firefox OS will include a personalized navigation experience with features like local information, live traffic reports, favorites and history sections, share location capability, social integration and more.

“As the leading provider of HTML5 turn-by-turn navigation solutions, as soon as we learned about the Firefox OS, we decided to release a dedicated version,” said Shlomo Emanuel, AtlasCT’s CEO. “We are still looking into a number of possible business models for the services and this is one of the subjects we are discussing with mobile carriers that have already committed to backing the open web device initiative.”

EverNav for Firefox OS will be available both on the Firefox Marketplace and on the EverNav website.

The addition of the BeiDou constellation is part of CSR’s ongoing efforts to support all global navigation satellite systems as they become available, with software or firmware upgrades, for greater performance and enhanced compliance with existing and future requirements of national GNSS systems, the company said.

“CSR is committed to supporting all current and future GNSS constellations with its location platforms to boost location performance by increasing service availability, reducing observation time and making measurements more precise for the most demanding applications,” said Dave Huntingford, director of marketing for location at CSR. “With the addition of these new satellites, our location platforms can now actively utilize GPS, GLONASS, QZSS and SBAS, in addition to BeiDou-2, and they are ready to support Galileo as soon as it becomes available to provide continuous location awareness and the best location-based services experience.”

Rob Yeh, director of product marketing for Automotive SoC at CSR, added, “All CSR’s latest multi-GNSS location platforms, including CSR SiRFatlasVI and SiRFprimaII, are now able to demonstrate live BDS (BeiDou System) navigation, and CSR will include BDS support in all future-generation location platforms. Besides providing flexibility and improved satellite acquisition and location tracking in challenging situations like urban canyons, the BeiDou support also improves CSR’s already industry-leading dead-reckoning technologies.”

CSR maintains an experienced development team in mainland China to develop and support BeiDou-related products and technology.

Also known as Compass and BeiDou-2, the Chinese BDS started operations in December 2012 and  has 14 active satellites in service over the Asia-Pacific region available to general users. When fully deployed by 2020, BDS is expected to comprise a total of 35 satellites offering complete coverage around the globe.

M100 Smart Glasses

Vuzix Corporation today announced that it has begun shipping M100 Smart Glasses to the first of its Gold developer partners — enabling them to start creating and testing their apps on the real hardware.

The M100 Smart Glasses are a smart hands-free display and communications device for mobile data access, once paired to a smartphone and connected to the Internet. The glasses include an integrated head tracker and GPS for spatial and positional awareness.

Vuzix is a supplier of Video Eyewear products in the consumer, commercial and entertainment markets.

Google Glass

The M100 is in competition — and a race to market — with Google Glass, a similar wearable device. Google recently held a contest to provide sample sets of the glasses to non-developers willing to pay $1,500 — which encouraged Internet and media buzz.

Google Glass could be released to the mass market by the end of the year. Google Inc. already sold an unspecified number of the glasses to developers who also paid $1,500 apiece at a company conference in June 2012. The mass-market version of Google Glass is expected to cost less than $1,500, but more than a smartphone.

Like the M100, Google Glass is intended to perform many of the same tasks as smartphones. The glasses include a little display screen attached to a rim above the right eye, run on Google’s Android operating system, and respond to voice commands, which is intended to make it easier for people to take pictures or record video wherever they might be (such as skydiving or riding a rollercoaster). Here is a video showing the glasses in action:

When he demonstrated the glasses at last June’s company conference, Google co-founder Sergey Brin acknowledged the company was still working out bugs and trying to figure out how to extend the product’s battery life.

Privacy Concerns. The ease of taking pictures and recording video with the glasses is causing some to question whether privacy will be affected.  zdnet blogger Ben Woods writes, “These glasses can instantly capture and store every move of everyone around the person wearing them. Remember that drunken argument you had with your partner? Well, now Google Glass will mean you have no possibility of forgetting it. If it’s entertaining enough, or you’re well-known enough, the video of that argument could well be on YouTube before you get home. Do you do a lot of business on the phone while out and about or while sitting in coffee shops? Will you continue to, if you know that every call could be recorded by the stranger sitting at the table opposite, staring innocently at the picture on the wall behind your head?”

Google first began developing the glasses in 2010 as part of a secretive company division now known as Google X.

How the M100 is worn.

Vuzix Showstopper. Displayed at Mobile World Congress in February as a “Showstopper,” the M100 contains a near-eye micro display with an integrated camera and powerful processor running an Android OS. It connects wirelessly to a user’s smartphone (iOS or Android) or other compatible device via Bluetooth or Wi-Fi, can connect directly to the Internet, and run applications and games on its own. Working in harmony with a user’s smartphone, the M100 enables access to a vast array of existing and future text, video, GPS, and audio applications, Vuzix said.

With the glasses, users can answer the phone using a visual address book, record video and run applications, including basic augmented reality apps. Interactive tracking and an integrated camera, combined with newly developed applications on the M100 and a wireless link to the Cloud, enable the merging of virtual information with the real world. An integrated camera enables video recording, still image capture and the potential for powerful augmented reality applications.

Industry, Medical. “Although we are seeing applications developed in most every market, there has been a strong focus on the industrial and medical markets,” said Paul J. Travers, chief executive officer.

“Our Company has a focus on developing the fundamental tools that enable applications from training to warehousing,” said Pete Wassell, president of Augmate Corporation, one of the first M100 Gold developers. “This new category of device is going to revolutionize many markets by injecting cloud-connected, hands-free and geospatially accurate information to applications that desperately need it. The M100 does a great job of delivering on that promise.”

The Vuzix developer program offers early access to the M100 smart glasses, technical support and advice. The M100 software developers kit is available in two versions, Gold and Silver. These SDKs are being delivered in stages and include frequent updates, hardware advances when released, and access to the developer center to provide technical and developer community support.

Because the demand is strong, Vuzix is delivering the first smart glasses on a first-come, first-served basis with custom-built prototypes going exclusively to its Gold Developers.

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.

• MOTOROLA Xoom Wi-Fi MZ604 – Android 3.2
• SAMSUNG Galaxy Nexus GT-I9250 – Android 4.0
• SAMSUNG Galaxy Note GT-N7000 – Android 2.3
• Blackberry 16GB Playbook – QNX 2.0.1.358 (pictured)
• SAMSUNG Galaxy S III – Android 4.0.4 (pictured)

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.

Manufacturers

The T-PN was developed by Trusted Positioning, Inc., of Calgary, Alberta, Canada.

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.

Following the recent announcement of Magellan’s SmartGPS device, the free Magellan SmartGPS Apps for iOS and Android devices are the next key elements in Magellan’s Smart Ecosystem, a cloud platform that integrates social media and navigation content directly onto a navigation map, the company said. The SmartGPS Apps automatically deliver continually updating reviews and tips for local businesses from social media including Yelp, Foursquare, and other partners to create current, local and personalized driving and pedestrian experiences.

The Magellan SmartGPS mobile apps display location-relevant information “squares” that graphically flip to show reviews, tips and offers from Yelp and Foursquare for nearby restaurants, stores and services. Users can then navigate to those locations directly from the SmartGPS App without needing to open an additional application or device. The cloud architecture enables new monetization of end users’ mobile search and navigation, and additional social media and content partners.

“We architected the Smart Ecosystem to integrate with automotive infotainment and mobile network service platforms so users can enjoy a truly mobile, connected car experience now,” said Peggy Fong, president of MiTAC Digital Corporation. “SmartGPS mobile apps connect to the vehicle dash, allowing users to easily search social media and points-of-interest for destinations, and send the locations via Bluetooth or Wi-Fi to SmartGPS-enabled vehicle navigation systems.”

Magellan’s free iOS and Android SmartGPS apps create a total-solution SmartGPS experience that is truly mobile. Magellan connects the smartphone to the vehicle dashboard, enabling location sync and sharing, hands-free operation and data connectivity. Users can pair their Magellan SmartGPS app with SmartGPS-enabled navigation systems. Using their SmartGPS App, SmartGPS enabled navigation system, or PC, users can search for a location, save the location in Magellan’s Smart Ecosystem cloud, and sync and share the location to any SmartGPS enabled device via Wi-Fi or Bluetooth.

The free Magellan SmartGPS Apps will be available in North America this Spring, and in Europe this Summer, from iTunes and Google Play. Premium versions of both apps featuring spoken turn-by-turn navigation will also be available.

The Avenza PDF Maps app takes advantage of geospatial technology and allows users to view, acquire and interact with maps on their mobile devices, including iPhone and iPad, without needing a mobile data connection or being accessed international roaming charges. PDF Maps offers an in-app store to facilitate the transaction and delivery of the maps, consolidating, in a digital format, consumers’ access to hundreds of maps from multiple publishers.

“In the last decade, advances in technology have shifted how consumers receive and use information, and we have responded by making our rich map content available on a variety of platforms,” said Charles Regan of National Geographic Maps. “Avenza’s PDF Maps app provides a unique way for consumers to access our content with an easy-to-use in-app map store and a set of robust features that will enhance the map user’s experience.”

Hundreds of maps from National Geographic Maps’ extensive library are now available in Avenza’s PDF Maps system, including travel and destination titles covering five continents, historical and thematic maps, and educational and reference titles. The app provides constant access to geographic information and points of interest, with additional interactive tools such as measuring, place marking and location tagging. PDF Maps operates without the risk of lost reception, due to cell tower proximity, and does not rely on an Internet connection.