I just reposted it, so even more will get use of it. The code can be found here:

https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter

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 while the accelerometer measures acceleration (g's) 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/deg/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. 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 -π to π (see http://en.wikipedia.org/wiki/Atan2), I simply add π, so the range it converted to 0 to 2π.

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.

**Sources:**

http://www.instructables.com/id/Accelerometer-Gyro-Tutorial/

http://www.arduino.cc/cgi-bin/yabb2/YaBB.pl?num=1284738418

http://www.x-firm.com/?page_id=148

http://web.mit.edu/first/segway/

**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.

I have attached my code, both the updated code for the Arduino and the Processing code. It is also possible to see the data from the y-axis. Just uncomment drawAxisY(); in the code.

**Newest firmware**

I decided to put all the source code on github, as it is much easier to maintain.

The newest code can now be found at github: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter

**My Balancing robot**

Below is a video of my balancing robot. It uses the same IMU and algorithm as described in the post above.

**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.

The best explanation that I have seen online so far.

Thx for the amazing effort.

Hi I wanted to use the IMU data with camera for vision based navigation,I need help regarding this,and IMU is 9DOF sensor stick from sparkfun and camera is Link sprite JPEG camera

Modeling accelerometer and gyroscope in simulink

Hi everyone , i'm working on a tracking system project that will localise people inside

a building during their mouvements using the IMU : inertial measurement unit (gyroscope

+ accelerometer) , and i have chosen the kalman filter algorithm to read the output of

the IMU to estimate and update the actual position

i need if it possible a module in simulink that simulate the gyroscope and accelerometer

and also how to implement the algorthm using kalman filter

thanks in advance :)

what part is the gyro i have an wireless remote/keyboard i want to disable the air-mouse gyro in well add a switch really

Hey, I am working on a project called wireless inertial pointer. Its a device just like a wireless mouse which can be used to control the cursor(pointer) on screen. I have used Arduino UNO, MPU 9150 and I am combining my gyro and accelerometer data using complimentary filter.

Everything is working well but my cursor on the screen is not moving smoothly, can you please help me with the mouse code.

my code for implementing mouse function is:

if(compAngleX > 90)

compAngleX = 90;

if(compAngleX <-90)

compAngleX = -90;

if(compAngleY > 95)

compAngleY = 95;

if(compAngleY <-95)

compAngleY = -95;

int x = map(compAngleY, -90, 90, -10, 10);

int y = map(compAngleX, -90, 90, -10, 10);

Mouse.move(x,y,0);

Hi,

I am a newbie in this area. How is the accelerometer used for measuring angles when the subject is in dynamic condition. I'm confused about this fact???

Very detailed article and very useful, your post makes me more motivated to do the balancing robot. Thank you very much!

wow.wow .that's really really cool

if i put the code as you give in link below,

https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter/blob/00c0bbfddbc27a302f29f22df8318959aa344435/IMU6DOF/MPU6050/MPU6050.ino#L39

then the error "sketch_nov24a:45: error: 'i2cWrite' was not declared in this scope"

now i confused which code i have to use..

i am totally zero..

The correct is this one: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter/tree/00c0bbfddbc27a302f29f22df8318959aa344435/IMU6DOF/LPR530AL_LY530ALH_ADXL335.

is the code suitable with my IMU?

http://www.cytron.com.my/viewProduct.php?pcode=SN-IMU5D-LC&name=5%20DOF%20IMU

maybe i need to set something there?

Camera control/stabilisation.

By using tri-axis gyroscope,tri-axis accelerometer,tri-axis megnetometer, and interfacing with Real-time control interface x-io board..kindly help please how i can buil this?

It must match this pinout: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter/blob/00c0bbfddbc27a302f29f22df8318959aa344435/IMU6DOF/LPR530AL_LY530ALH_ADXL335/LPR530AL_LY530ALH_ADXL335.ino#L23-L29.

Also try to print this value: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter/blob/00c0bbfddbc27a302f29f22df8318959aa344435/IMU6DOF/LPR530AL_LY530ALH_ADXL335/LPR530AL_LY530ALH_ADXL335.ino#L86.

Acc_Gyro Arduino

5V <—> 5V

GND <—> GND

Gx4 X <—> AN0

Gx4 Y <—> AN1

Acc X <—> AN3

Acc Y <—> AN4

Acc Z <—> AN5

when i try to print "accXval" and "accYval".. they display zero.

maybe that is the problem..

Camera control/stabilisation.

By using tri-axis gyroscope,tri-axis accelerometer,tri-axis megnetometer, and interfacing with Real-time control interface x-io board..kindly help please how i can buil this?

Acc_Gyro Arduino

5V <—> 5V

GND <—> GND

Gx4 X <—> AN0

Gx4 Y <—> AN1

Acc X <—> AN3

Acc Y <—> AN4

i dont use z-axis..

i just straight put in your code.. but the serial monitor display zero..

what the problem? do i need to set anything else before upload to arduino?

It must match this value: https://github.com/TKJElectronics/Example-Sketch-for-IMU-including-Kalman-filter/blob/00c0bbfddbc27a302f29f22df8318959aa344435/IMU6DOF/MPU6050/MPU6050.ino#L39.

how to connect my imu..

i have these pins:

1) 5v

2) GND

3) X-acc

4) Y-acc

5) Z-acc

6) X-ratex4.7(inv)

7) Y-ratex4.7(inv)

8) X-rate

9) Y-rate

10) ST-acc

If looks like it is just a regular analog IMU, so simply connect it as described in this guide.

Regards

Lauszus

first of all thanks for such clear and simple instructions.

you wrote that the gyro readings are precise but it drifts away with time, my question is in my project the gyro's orientation will be changing in almost every second to every minute(max), is the drifting problem still an issue for my application i.e. does the value drifts away when the orientation is kept same for long or does it drifts anyway on very long periods of use, and also what is the approximate period for significant drift.

thanks in advance!

If the filter is properly tuned then the output from the Kalman filter is very precise.

You should have a look at the following blog I wrote: http://blog.tkjelectronics.dk/2012/09/a-practical-approach-to-kalman-filter-and-how-to-implement-it/.

Regards

Lauszus

Regards

Lauszus

Sorry for my dummines :confused:

need fast and better sugestions...

Are these the steps of PID Tuning?

dv=kp*angle+ki*integral+kd*diff;

integral+=angle;

diff=angle-prev_angle;

prev_angle=angle;

oc0=(int)dv;

You will need to find the Kp, Ki and Kd values.

once again! i'm here with you.

As i told you in my previous conversation, i was trying to make the self balancing robot using AVR platform but some of the problem.My robot was not working properly so again i need your help.

I got the PID result on my MATLAB Software but unfortunately,i'm not able to tune the graph of PID so I need your help to tune the parameter of Kp,Kd,Ki of PID to balance the robot.

if u know any software to direct tune PID using graph then it will be really helpful for me.

Also check out my Kickstarter campaign: http://www.kickstarter.com/projects/tkjelectronics/balanduino-balancing-robot-kit. It's a complete kit with all you need to build a balancing robot.

The only difference between the MPU-6000 and the MPU-6050 is that the MPU-6000 supports SPI as well. So you can use my code just fine.

Your suggestions really helped!

You can see how to implement the integral term here: https://github.com/TKJElectronics/BalancingRobotArduino/blob/master/BalancingRobotArduino.ino#L178.

Your suggestions really helped!