Hallo everybody I recently bought this analog 6DOF (six degrees of freedom) IMU board (http://www.sparkfun.com/products/10010) from watterott.com. It uses three gyros and three accelerometers to calculate angles in three dimensions.
I looked a while for some code online and how to connect with them. After many hours of research I succeeded of making af precise measurement of angles in two directions. I decided to write a short guide for fellow electronic enthusiasts. The main purpose of this guide is to teach others how to get some useful data from their IMU or just a gyro or accelerometer. The code for Arduino can be found at github: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter. It should be pretty easy to implement my code to your own sensor. I will not describe all the details about the theory behind, instead you can look at the sources for more info.
Before you begin you have to connect the IMU as follows:
Acc_Gyro Arduino 3.3V <—> 3.3V GND <—> GND Gx4 X <—> AN0 Gx4 Y <—> AN1 Gx4 Z <—> AN2 Acc X <—> AN3 Acc Y <—> AN4 Acc Z <—> AN5
Also connect 3.3V to the AREF pin on the Arduino for more accuracy. It is VERY important that you do not connect the sensor to 5V - this will destroy the sensor.
Now your are ready for reading some data from the sensor.
To communicate with the sensor is straightforward: The gyro measures degrees per second (0/s) while the accelerometer measures acceleration (g'a) in three dimensions. Both outputs the measurements as a analog signal. To get these translated into degrees you have to do some coding:
The gyro First you have to translate quids (a number from 0-1023) into something useful (this is for a ADC with a 10 bit resolution, for example this should be 4095 (2^12-1=4095) for 12 bit ADC). To do this I just use this simple equation: gyroRate = (gyroAdc-gyroZero)/sensitivity - where gyroAdc are the readed value from our sensor, gyroZero is the value when it is stationary (this is done in the code - look in the "Setup" section) while sensitivity is the sensitivity found in the datasheet, but translated into quids.
If you look in the two gyros datasheets (http://www.sparkfun.com/datasheets/Sensors/IMU/lpr530al.pdf and http://www.sparkfun.com/datasheets/Sensors/IMU/LY530ALH.pdf) you will see that the sensitivity is 3.33mV/0/s for the 4xOUT. To translate these into quids is pretty easy: sensitivity/3.3*1023. So in this example I get: 0.00333/3.3*1023=1.0323.
NB: to translate mV to V simple just divide by one thousand.
The final equation will look like this: gyroRate = (gyroAdc-gryoZero)/1.0323
The result will come out as degrees per second (0/s). To translate this into degrees you have to know the exact time since the last loop. Fortunately, the Arduino got a simple command to do so: millis(). By using that, one can calculate the time difference (delta time) and thereby calculate the angle of the gyro. The final equation will look like this: gyroAngle += gyroRate*dtime/1000
Unfortunately, the gyro drifts over time. That means it can not be trusted for a longer timespan, but it is very precise for a short time. This is when the accelerometer comes in handy. It does not have any drift, but it is too unstable for shorter timespan. I will describe how to combine these measurements in a while, but first I will describe how to translate the readings from the accelerometer into something useful.
The accelerometer The accelerometer measures the acceleration (g's) in three dimensions. To translate the analog readings into degrees you simply need to read the axis and to subtract the zero offset like so: accVal = accAdc-accZero
Where accAdc is the analog reading and accZero is the value when it reads 0g - this is calculated in the start of the code, look in the "Setup" section. The zero value can also be found in the datasheet: http://www.sparkfun.com/datasheets/Components/SMD/adxl335.pdf. You will see that the zero voltage at 0g is approximately 1.5V, to translate this into quids, you again have to use this equation: zeroVoltage/3.3*1023. So in this example I get: 1.5/3.3*1023=465.
You can then calculate the pitch and roll using the following equations: pitch = atan2(accYval, accZval)+PI roll = atan2(accXval, accZval)+PI
Atan2 has a output range from -? to ? (see http://en.wikipedia.org/wiki/Atan2), I simply add ?, so the range it converted to 0 to 2?. To convert it from radians to degrees we simply multiply the result by 57.295779513082320876798154814105 - this is predefined in the Arduino IDE as RAD_TO_DEG.
Kalman filter As I explained earlier the gyro is very precise, but tend to drift. The accelerometer is a bit unstable, but does not drift. You can calculate the precise angle by using something called a Kalman filter. A detailed guide on how it's implemented can be found at my blog: http://blog.tkjelectronics.dk/2012/09/a-practical-approach-to-kalman-filter-and-how-to-implement-it/.
If you want to use something a bit more simple, you can use what's called a Complementary Filter. It is pretty easy to understand and the math is much simpler, because it only works in one step. For example the equation could look like this: angle = 0.98 *(angle+gyro*dt) + 0.02*acc - you can fine tune the numbers to whatever you like. Just remember that the sum must be 1. For me the result from the Complementary Filter was very close (or almost the same) as the one calculated by the Kalman filter.
You have now learned (hopefully) how to get analog data from IMU and translate it to something useful. I have attached my own code for my 6DOF IMU (http://www.sparkfun.com/products/10010), but with some slightly modification, I am pretty sure that it is possible to use it with any analog gyro/accelerometer.
If you have any question, fell free to post a comment below.
Update I have just finished a Processing code which prints out data from the Arduino on a nice graph. As you can see in the video below the filtering is quit effective. The light blue line is the accelerometer, the purple line is the gyro, the black line is the angle calculated by the Complementary Filter, and the red line is the angle calculated by the Kalman filter. As you might see the Kalman filter is just a bit more precise (i know it is difficult to see in the video) than the Complementary Filter, especially when I shake it. The code can be found at github: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter/tree/master/Graph. It is also possible to see the data from the y-axis. Just uncomment drawAxisY(); in the code.
My Balancing robot Below is a video of my balancing robot. It uses the same IMU and algorithm as described in the post above. The source code can be found at the following link: https://github.com/TKJElectronics/BalancingRobotArduino http://www.youtube.com/watch?v=N28C_JqVhGU
Kickstarter I have just released my balancing robot on Kickstarter: http://www.kickstarter.com/projects/tkjelectronics/balanduino-balancing-robot-kit. Please consider backing the project. http://www.youtube.com/watch?v=_kQniPbg9zc
Update I have also provided code for several other IMUs including a 9DOF which allows you to estimate yaw as well. All can be found in the Github repository: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter.