Accurate Navigation Without GPS

Circuit keeps up to 5.5-meter accuracy after 3 kilometers

2 min read
Photograph of the personal GPS boot created at the University of Utah.
Photo: Qingbo Guo

The Global Positioning System can locate you within 5 to 10 meters anywhere on Earth—as long as your receiver is in the line of sight of multiple satellites. Getting location information indoors is tricky. A team at the University of Utah has now put the solution underfoot: A suite of sensors and circuits mounted to a boot can determine position with an accuracy of about 5 meters, indoors or out, without GPS.

The navigation system, installed in a very hefty prototype boot, could help rescue workers navigate inside buildings, and show firefighters where their team members are. It might also be integrated with virtual or augmented-reality games. The Utah researchers presented their GPS-free navigation system on Tuesday at the International Solid-State Circuits Conference in San Francisco.

The Utah group’s navigation system is built on an inertial measurement unit, or IMU—a little black box containing a gyroscope, magnetometer, and accelerometer. High-end systems of this kind help airplane pilots navigate. Darrin Young, an electrical engineer at the University of Utah, wanted to make it possible to use an IMU in portable electronics. But readings from cheap versions of these sensors drift over time, and errors can accumulate rapidly. “If you’re sitting still, the reading should be zero,” says Young. But after 5 or 10 minutes sitting in a chair, the low-end IMU might think you have moved a few hundred feet away.

Left: Qingbo Guo walking outdoors with the system; Right: Labeled diagram of the boot.Photos: Qingbo Guo

Young asked graduate student Qingbo Guo to figure out a way to keep these sensors calibrated. Guo found the solution in biomechanics. During each step, the heel is anchored to the ground for about 100 milliseconds. Guo figured out how to measure this instant of stillness, and use that to correct for the false motion in drifting data from the IMU. “You reset the position calculation with every step, so you do not accumulate error,” says Young.

Guo designed a flexible MEMS pressure sensor to place under the insole of a boot with an IMU. He calculated that the system needed about 1,000 sensors to get accurate readings (and provide redundancy in case some sensors broke underfoot), built a custom circuit to combine data from the IMU and the pressure sensors, and designed the necessary algorithms. In the prototype, the custom circuit is on a printed circuit board mounted to the side of the boot. “We put it outside the boot for easy access and debugging,” says Guo. “But it could be inside the sole, and use Bluetooth.”

Map showing the trajectory of Qingbo Guo's wallk.Image: Qingbo Guo

Then he connected it all up with cables to a laptop in his backpack and started walking. To test the navigation system, Guo walked in Salt Lake City for about 3 kilometers with the navigational boot tracker, then compared data from the prototype with GPS data. After 10 walks around campus, the maximum error was about 5.5 meters. He also tested the system during a longer walk in San Francisco’s Golden Gate Park.

It doesn’t matter what kind of terrain—the system works. “The performance is comparable to GPS, but it works inside,” he says.

The Conversation (0)

Self-Driving Cars Work Better With Smart Roads

Intelligent infrastructure makes autonomous driving safer and less expensive

9 min read
A photograph shows a single car headed toward the viewer on the rightmost lane of a three-lane road that is bounded by grassy parkways, one side of which is planted with trees. In the foreground a black vertical pole is topped by a crossbeam bearing various instruments. 

This test unit, in a suburb of Shanghai, detects and tracks traffic merging from a side road onto a major road, using a camera, a lidar, a radar, a communication unit, and a computer.

Shaoshan Liu

Enormous efforts have been made in the past two decades to create a car that can use sensors and artificial intelligence to model its environment and plot a safe driving path. Yet even today the technology works well only in areas like campuses, which have limited roads to map and minimal traffic to master. It still can’t manage busy, unfamiliar, or unpredictable roads. For now, at least, there is only so much sensory power and intelligence that can go into a car.

To solve this problem, we must turn it around: We must put more of the smarts into the infrastructure—we must make the road smart.

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