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Sensor Fusion on Android Devices: A Revolution in Motion Processing

Google TechTalks

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  • Recommended by 2 people: 2 on Hacker News.
  • A classic. People kept recommending it from 2015 to 2026.
  • Presented by David Sachs, InvenSense
  • May be out of date. Uploaded 16 years ago, in an area where tools change fast.

Intermediate. Assumes working knowledge.

517K views16 years ago2 recommendations

HardwareMobile developmentEmbedded systemsAndroid and Jetpack ComposeAlgorithms

David Sachs explains how gyroscopes, accelerometers, and compasses work, demonstrating sensor fusion algorithms and the challenges of dead reckoning.

Google Tech Talk August 2, 2010 ABSTRACT Presented by David Sachs. Gyroscopes, accelerometers, and compasses are increasingly prevalent in mainstream consumer electronics. Applications of these sensors include user interface, augmented reality, gaming, image stabilization, and navigation. This talk will demonstrate how all three sensor types work separately and in conjunction on a modified Android handset running a modified sensor API, then explain how algorithms are used to enable a multitude of applications. Application developers who wish to make sense of rotational motion must master Euler angles, rotation matrices, and quaternions. Under the hood, sensor fusion algorithms must be used in order to create responsive, accurate, and low noise descriptions of motion. Reducing sensing errors involves compensating for temperature changes, magnetic disturbances, and sharp accelerations. Some of these algorithms must run at a very high rate and with very precise timing, which makes them difficult to implement within low-power real-time operating systems. Within Android specifically, this involves modifying the sensor manager, introducing new APIs, and partitioning motion processing tasks. David Sachs began developing motion processing systems as a graduate student at the MIT Media Lab. His research there led him to InvenSense, where he continues this work with MEMS inertial sensors used in products such as the Nintendo Wii Motion Plus. David's designs incorporate gyroscopes, accelerometers, and compasses in various combinations and contexts including handset user interfaces, image stabilizers, navigation systems, game controllers, novel Braille displays, and musical instruments.

Why it's here

2 people recommend this

What people say. Highly recommended as the definitive explanation of sensor fusion and why double-integrating accelerometer data to track position fails in practice.

gregsadetskyon Hacker News • Recommends

On “Show HN: Physically accurate black hole you can put in your room”

When this topic comes up, I'm always happy to share this incredible lecture from many many years ago about dead reckoning and how you _cannot_ integrate acceleration twice: link to this video (the whole video is incredible, but this is the magical bit)

cpitmanon Hacker News • Recommends

On “Darpa to re-invent GPS navigation without the use of satellites”

Then you would be surprised how bad they are. A great talk on using mobile sensors is here: link to this video Long story short, expect something like 20 cm/s of position drift trying to do dead reckoning off of mobile sensors.

losvediron Hacker News • Mentions

On “You’ll Download Physical Objects Sooner Than You Think”

…lerometers to make the double integration to position meaningful. Something like Sensor Fusion1 can make it more accurate by incorporating data from the gyroscope and compass, but even so it's not enough. In any case, like you I hope someone develops something intuitive to really help 3D printing take off. The future should be interesting... 1 link to this video…

FabHKon Hacker News • Mentions

On “Plans to shift aviation from magnetic navigation to true navigation”

…ve any noise or error, you're integrating that, and integrate that again. Hello, parabola. 2. Gravity. It's strong. So you have to subtract it (as it induces an apparent acceleration upwards). If the difference between actual down and where your model thinks is down is just a fraction of a degree, you'll be totally off within minutes. See eg here: link to this video Or here: link > As a concrete example consider a tilt error of just 0.05 degrees. This error will cause a component of the acceleration due to gravity with magnitude 0.0086 m/s2 to be projected onto the horizontal axes. This residual bias causes an error in the horizontal position which grows quadratically to 7.7 m after only 30 seconds…