This week, I worked on code for reading in the IMU data and mapping this to mouse movements. Working with Rosina, I experimented with the calibrated linear acceleration data coming from the IMU. We noticed that our data was decently accurate thanks to the IMU’s software for bias-reduction and combating drift. We used this data accuracy to our advantage when designing our position calculator. We utilized a 3-state Kalman filter with position, velocity, and acceleration as our states. On each iteration of the filter, we used a double integration method to calculate the position.
When experimenting with this, we noticed that the Kalman filter resulted in a good amount of smoothing of our position curve, but the position would shift back a little bit due to our velocity values. To correct for this, we implemented code that would snap the velocity to zero if several acceleration measurements of 0 were detected. That way the drift would be minimal. When we implemented our mouse movement code, this resulted in pretty good movement. In order to make this as accurate as possible, we plan on using our failsafe button as an interrupt to snap the mouse position in place and avoid the recoil. Now that this works for a single dimension, we plan to implement a second kalman filter to handle vertical movement at the same time.
In terms of the Gantt chart, I still have a lot of progress to make towards polishing our product, but a lot of the main challenges are completed.