Sensor Fusion
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- Posts: 67
- Joined: Sun Dec 11, 2016 1:07 am
Sensor Fusion
Are there future plans to include sensor fusion to combine GPS, accelerometer, gyro and magnetometer readings in the app to improve the precision and accuracy of data? This sort of thing is done for drone flight control, for example, using inexpensive 9 or 10 degree of freedom sensors and GPS.
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- Posts: 67
- Joined: Sun Dec 11, 2016 1:07 am
Try some of these:
http://www.starlino.com/imu_guide.html
http://www.starlino.com/dcm_tutorial.html
Direction cosine matrices are better than Euler angles (roll, pitch and yaw), which can suffer what's known as gimbal lock. https://en.wikipedia.org/wiki/Gimbal_lock
http://diydrones.com/profiles/blogs/a-s ... dreckoning
The link to Matrix Pilot in the above is broken. It's here:
https://github.com/MatrixPilot/MatrixPilot/wiki
http://www.pieter-jan.com/node/11 (using a complementary filter instead of a Kalman filter)
http://www.pieter-jan.com/node/7 (integrating rotation rate data to get direction)
The explanation of drift in some of the above is wrong. The problem is that when you integrate noisy data, digitally or otherwise, you get what's called a random walk or a unit root. That means that the range of error in velocity or angle when integrating accelerometer or rotation rate data increases in proportion to the square root of time. The error increases even faster when you integrate accelerometer data twice to calculate position. That's why you need a GPS to provide drift correction and for something like a quadcopter which can hover, a magnetometer to provide a fixed direction reference.
Submarine and missile inertial guidance systems use extremely low noise IMU's based on things like ring laser gyro's or extremely precise. rapidly rotating beryllium spheres so the drift is small. That sort of thing is obviously overkill for logging vehicle data.
http://www.starlino.com/imu_guide.html
http://www.starlino.com/dcm_tutorial.html
Direction cosine matrices are better than Euler angles (roll, pitch and yaw), which can suffer what's known as gimbal lock. https://en.wikipedia.org/wiki/Gimbal_lock
http://diydrones.com/profiles/blogs/a-s ... dreckoning
The link to Matrix Pilot in the above is broken. It's here:
https://github.com/MatrixPilot/MatrixPilot/wiki
http://www.pieter-jan.com/node/11 (using a complementary filter instead of a Kalman filter)
http://www.pieter-jan.com/node/7 (integrating rotation rate data to get direction)
The explanation of drift in some of the above is wrong. The problem is that when you integrate noisy data, digitally or otherwise, you get what's called a random walk or a unit root. That means that the range of error in velocity or angle when integrating accelerometer or rotation rate data increases in proportion to the square root of time. The error increases even faster when you integrate accelerometer data twice to calculate position. That's why you need a GPS to provide drift correction and for something like a quadcopter which can hover, a magnetometer to provide a fixed direction reference.
Submarine and missile inertial guidance systems use extremely low noise IMU's based on things like ring laser gyro's or extremely precise. rapidly rotating beryllium spheres so the drift is small. That sort of thing is obviously overkill for logging vehicle data.
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- Posts: 67
- Joined: Sun Dec 11, 2016 1:07 am