Download new sensor fusion release

Download new sensor fusion release


The last time I blogged about Freescale’s sensor fusion solution (Open source sensor fusion) was almost a year ago.  Since then, it’s been downloaded roughly 2,000 times and we have a very active Freescale Sensor Fusion Community.  I would like to thank everyone who has taken the time to check it out.  I would also like to invite you to download our new (Version 5.00) release, posted just this month.  Features of the new release include:

  • New and improved 6-axis and 9-axis Kalman filters
  • Addition of FRDM-K22F support for KDS
  • Additional “bonus” bare board projects for 3-axis tilt and 6-axis eCompass
  • Updated documentation, including data sheet, user guide and full details of the Kalman algorithms
  • All source code included under BSD 3-clause open source license
  • Community support at http://community.freescale.com/community/sensors/sensorfusion

An updated Freescale Sensor Fusion Toolbox for Windows supporting all of the above is also available.  Make sure you download this at the same time you get the library, as both have changed.  New versions of library/toolbox are not compatible with old versions of toolbox/library.

By far the biggest change the list above is the first bullet.  We’ve changed how the filters track linear acceleration and magnetic interference.  In the previous generation filters, we tracked these explicitly as state variables for the system.  In the new filters, we treat them as noise and adjust weighting factors and co-variances based upon how much acceleration and magnetic field differ from expected values.

We use those weighting factors within a least squares fit of known vs measured gravity and magnetic vectors.  This technique was first proposed back in the 1960s by Grace Wahba as part of NASAs work in determining satellite attitude as a function of observed versus known star locations.  Using this technique gives us a very elegant way of automatically handling magnetic interference and effects of linear acceleration by biasing our orientation estimate one way or the other.  From the computed orientation, we can extract new estimates for gravity and magnetic field, which are then compared with gyro-compensated estimates from the last iteration of the filter.  Those errors then become inputs to our Kalman filter.  The overall process is illustrated in the figure above.  Not shown is the hard/soft iron compensation step, which precedes the Wahba calculation.

Don’t worry if none of that makes sense right away.  But if you take the time to work through the math, you’ll find this figure will act as a handy frame of reference.  And when you try the new filters, you’ll discover that they converge faster and are generally more stable.

We only do “full web releases” on a periodic basis.  Bug reports and new features generally show up on the community site first.  Be sure to check in there before starting any new development.

Our BSD 3-clause open source license continues to apply, so you can (still) quickly add unique features to your products without worrying about pesky license restrictions.  And finally, the new library and sensor fusion toolbox are available for download now at http://www.freescale.com/sensorfusion.

Michael Stanley “works” on fun sensors and systems topics at Freescale.

 

Michael Stanley
Michael Stanley
Mike Stanley develops advanced algorithms and applications for MCUs and sensors, including sensor fusion and sensor data analytics. He is a founding member of the MEMS Industry Group’s Accelerated Innovation Community and a contributor to the IEEE Standard for Sensor Performance Parameter Definitions (IEEE 2700-2014). He is co-author of a chapter on intelligent sensors in “Measurement, Instrumentation, and Sensors Handbook” (volume two), and speaks on sensor topics. When the Arizona temperature drops below 100 degrees, you'll find Mike flying his F450 quadcopter . Follow him @SensorFusion.

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