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Progress report

A small report on what I have done in a little less than a week’s time.

Practical todo’s

  1. Read out an IMU with a programming language or to file: I wrote an Arduino sketch, based on code from Pololu’s example programs for its gyroscope and accelerometer and magnetometer boards. I hooked up the Arduino with an LM35DZ temperature sensor, as the IMU’s sensitivity depends on the temperature. Data is read out by a simple Python script which writes it to file.

    Currently, I am reading out data while a breadboard with both sensors is stuck to a wall, such that it should be very stable. Data between the several sensors needs to be fused; the IMU chip doesn’t do this for you.

  2. Track a camera’s distance with respect to a marker: I installed ArUco and will work on this point after this post.

  3. Perform a simple kind of fusion between the tracked position and the IMU data: Not done yet.

Theoretical todo’s

  1. Read in on Kalman filters: I have watched and done the homework for several of the 2012 lectures of Cyrill Stachniss. I now understand the „ordinary” Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), extended information filter (EIF), and sparse extended information filter (SEIF). I will write a part for my thesis about the KF methods soon, which might be included in my final thesis.

  2. In Caarls’ PhD thesis, read up on specific Kalman filters and „continuous time processes”: Nothing done yet.

  3. Collect papers on IMU – (stereo)camera fusion: Nothing done yet.

References

  • Jurjen Caarls. Pose estimation for mobile devices and augmented reality. PhD thesis, Delft University of Technology, 2009. [ bib ]
    
@phdthesis{caarls2009pose,
  title = {Pose estimation for mobile devices and augmented reality},
  author = {Caarls, Jurjen},
  year = {2009},
  school = {Delft University of Technology}
}