3D Tracking with IMU

This video demonstrates an algorithm that enables tracking in 6DOF (pitch, roll, yaw, and x, y, z displacement) using only an IMU (gyroscope and accelerometer). The algorithm uses assumptions of gait overcome the problems with obtaining a position through the double integration of acceleration.

Data was collected using an x-IMU; a self contained unit with on-board gyroscope, accelerometer and SD-card logging. See: http://www.x-io.co.uk/node/9

The work presented was conducted by Sebastian O.H. Madgwick during his Ph.D research at the University of Bristol.

Category:

Science & Technology

Tags:

• IMU
• AHRS
• x-IMU
• SD card
• motion
• tracking
• gait
• walking
• MATLAB
• 6DOF
• 9DOF
• gyroscope
• accelerometer
• magnetometer
• Indoor GPS

[Tyrant001MrX]

Thanks for your fast answer. If it's the latency, How I can improve it?I want to get the same speed of the cube in the video. Sorry for asking the same cuestion in my last post, my doubt was to obtaining it from the quaternions of your algorithm.

Thanks for everything, I hope not to being so disturbing for you.

[SebMadgwickResearch]

@Tyrant001MrX Latency (a pure delay) has nothing to do with my algorithm so I can’t help you here. It is probably due to ‘slow software’ in the same way that a PC game would appear “laggy” on an under-spec’ed machine.

[afazzo]

Impressing...I'm trying to implement your algorithm on PC using a BMA-180 plus IT-3200 IMU, and I'm quite optimist about the result. I have just a residual "vibration" in the estimated orientation, possibily due to the very simple forward Euler time stepping scheme you used.

I have a question about the position tracking... I wasn't able to find anything about it on your Ph.D thesis . Which algorithm did you use? Did you measured the error of the position at the end of the walk? Thanks.

[SebMadgwickResearch]

@afazzo My Ph.D thesis has not been published yet. I don’t know if the algorithm I used has a name or is documented anywhere. The final position error was ~1m after a ~100 m circuit including 2 floors (inc. 2 stair cases). I have since discovered that this is an extremely low error and is not representative.

[SebMadgwickResearch]

@afazzo My Ph.D thesis has not been published yet. I don’t know if the algorithm I used has a name or is documented. The final position error was ~1m after the ~100m circuit including 2 floors (inc. 2 stair cases shown in video). I have since discovered that this is an extremely low error and is not representative.

[SebMadgwickResearch]

@Tyrant001MrX If it seems “laggy” then this will be due to latency, not the update frequency. I have already responded to your “Earth referenced forces” question.

[Tyrant001MrX]

@SebMadgwickResearch,Hi again, I'm testing your Quaternions IMU Algorithm, it works fine, but it seems to be more laggy than in the demo video, may be because I´m using a lower sample rate (100Hz, every 10ms), is that reason??. How I can obtain Earth referenced forces measured by accelerometers with the quaternions in order to eliminate gravity influence in the system?

Thanks again.

[Tyrant001MrX]

@SebMadgwickResearch Thanks, I will check those books in order to improve my knowledge in 3D inertial tracking.

[raydks]

@SebMadgwickResearch

sir, thanks for the answer, I try what u said and got a close result. I used the MARU u released in google project to calculate the gravity by multiplying the rotation matrix to the acceleration in each acceleration axis obtained at the beginning. However, there is still some DC gravity cannot be eliminated. Could u help? thanks a lot

[SebMadgwickResearch]

@Tyrant001MrX It is achieved though a rotation matrix multiplication, you would want to avoid using Euler angles in such operations. I cannot explain the mathematics here but it is covered in any relevant introductory textbook; e.g. Kuipers’ “Quaternions and rotational sequences”, or Craig's “Introduction to robotics...”.