By: Chris Goodall, Managing Director, InvenSense Canada
We’ve all been there. Heading somewhere new, relying on our GPS. We have our phones, so why bother looking up directions beforehand? “We’ll look on our phones to find terminal E when we make our connection.” “The restaurant is on Canyon Road, I’ll look at the map on my phone when we get in the car.” And before we know it, the beautiful, scenic drive to the restaurant with the great view, becomes the lost GPS highway. The quick connection in St. Louis becomes a panicked sprint to the flight monitors, feverishly looking for Terminal E.
Well, what if there’s a way to eliminate these types of situations all together, a way to still find our way when GPS and Wi-Fi aren’t options?
InvenSense Canada (formerly Trusted Positioning, having been acquired by InvenSense in 2014) seems to have a solution.
In 2009, Naser El-Sheimy was a professor in the Geomatics Engineering Department at the University of Calgary; Chris Goodall (TPI’s ex-CEO) and Zainab Syed were his graduate students. Jacques Georgy would also soon come on board from Queens University. The group started on inertial navigation by exploring tactical grade IMU’s (inertial measurement units) and integration with GPS. Around 2004, the topic of MEMS, and the opportunities that MEMS offered for navigation, started to become of interest. The availability of MEMS in mobile electronic devices began to proliferate in 2008 after the launch of the iPhone 3G, which opened up the possibility of mainstream inertial navigation for mobile phones.
With the money from two proposals that were accepted by Canadian government support agencies eventually becoming start-up money, TPI came to be. The pre-TPI research at the University of Calgary led to a couple of patents that went with the team into the new business, and as the business grew, new patents began to be developed, all involving navigating with sensors when GPS was unavailable. Also at the time, other cellphone and tablet manufacturers were adding MEMS inertial components so users could readily reorient screens and play motion video games. TPI began to use this proliferation of MEMS sensors for inertial aiding and inertial navigation for handheld personal navigation. InvenSense’s current technology isn’t limited to MEMS inertial sensors (accelerometers and gyroscopes) in phones; it also uses magnetometers, barometers, and available Wi-Fi networks and their associated location databases, GNSS, vehicle speed sensors, user updates, and camera inputs.
So what exactly does this technology do and how does it work?
As Chris Goodall explains, “[The technology] tracks motion when [users] go into areas where GPS doesn’t work. We do math and software. It is kind of the opposite of GPS. GPS uses absolute positioning, utilizing satellites to triangulate your position. We use relative positioning. When you enter a room and GPS stops working, our technology takes over by motion sensing your relative position based on a previously known point.” When GPS is turned off, the sensor positioning bridges the gaps. InvenSense offers an inertial navigation software that calculates the position of the device based on its movements between two GPS fixes. Therefore, GPS does not need to run continuously, expanding the battery life of mobiles and wearables. Duty cycling controls GPS on/off times but still provides a seamless solution to the user. The sensor solution in the device is always on when moving and providing constant accuracy output that integrates with all available updates. With this data, Wi-Fi location and other absolute positioning sources, such as magnetic fingerprint and beacons, can be used to update the relative inertial navigation system.
InvenSense has an entire mathematical profile of typical motion modes, such as walking/running, and use-cases, such as hand swinging, for how people and vehicles move with their cell phones. The library is based on algorithms that detect these particular movement profiles and then filter adjustments from absolute positioning sources to maintain or improve accuracy.
So what is the future of this technology?