Forum Discussion
Drift Correction Techniques
I'm working on a project where I will be using a motion capture tracking system to track the HMD (Oculus DK2). Now I'm curious because I could not really find any documentation on manual drift correc...
An approach like that could work reasonably well, but the general answer to this problem is a Kalman filter (an EKF is probably most applicable), it is purpose built for combining sensors with different capabilities (and sources of error) to get a smooth output with prediction. The details are pretty complex, but there is a lot of material out there as it is used all the time in robotics and control systems.
The general idea is to create a simulated model of your system, and apply statistical methods to remove noise and bias by comparing expected vs observed outputs. As a high level example, when you observe a certain displacement from a camera, you would expect to see a certain acceleration from your IMU, plus noise, and vice versa. How they correlate tells you a lot about the true state of the system, as in the real position at a given instant.
The general idea is to create a simulated model of your system, and apply statistical methods to remove noise and bias by comparing expected vs observed outputs. As a high level example, when you observe a certain displacement from a camera, you would expect to see a certain acceleration from your IMU, plus noise, and vice versa. How they correlate tells you a lot about the true state of the system, as in the real position at a given instant.