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Engineering

Rethinking GPS: Engineering Next-Gen Location at Uber

19 April 2018 / Global
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Figure 1: The above GIF offers a comparison of standard GPS (red) against our improved location estimate (blue) for a pickup from Uber HQ in San Francisco. Our estimated location closely follows the true path taken by the rider, while GPS shows very large excursions.
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Figure 2: In this simplified interpretation of GPS receiver computation, spheres intersect at the center of known satellite locations.
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Figure 3: Line-of-sight blockage and strong reflections can cause large GPS errors.
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Figure 4: Satellite signal strengths, when combined with 3D maps, provide valuable location information.
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Figure 5: Ray tracing from one possible location to each satellite for probabilistic shadow matching. This is done for thousands of hypothesized locations.
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Figure 6. A location heat map computed based on satellite signal strengths can have many hotspots. In the above example, our improved location estimate (blue path, black uncertainty ellipse) follows ground truth (yellow path), whereas standard GPS (red path, gray uncertainty ellipse) is inaccurate.
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Figure 8: Uber’s GPS improvement system is composed of a particle filter service, 3D map tile management service, a manager service, Uber HTTP API, and cloud storage, and integrates with other Uber services.
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Figure 9: Red dot/blue dot comparison on our internal version of the rider app allows Uber employees to spot check our solution anywhere in the world.
Danny Iland

Danny Iland

Danny Iland is a senior software engineer on Uber’s Sensing, Inference, and Research team.

Andrew Irish

Andrew Irish

Andrew Irish is a senior software engineer on Uber’s Sensing, Inference, and Research team.

Brian Sandler

Brian Sandler

Brian Sandler was a summer intern on Uber’s Sensing, Inference, and Research team and is currently a Ph.D student with the University of Pennsylvania.

Posted by Danny Iland, Andrew Irish, Upamanyu Madhow, Brian Sandler

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