Recursive Estimation of Orientation Based on the Bingham Distribution
This work addresses directional tracking issues in applications like robotics or navigation, but it is incremental as it extends existing methods to handle periodic data more effectively.
The paper tackled the problem of directional estimation in tracking applications by developing a recursive filter based on the Bingham distribution for two-dimensional data with 180-degree symmetry, and it outperformed traditional Kalman filters in a challenging scenario.
Directional estimation is a common problem in many tracking applications. Traditional filters such as the Kalman filter perform poorly because they fail to take the periodic nature of the problem into account. We present a recursive filter for directional data based on the Bingham distribution in two dimensions. The proposed filter can be applied to circular filtering problems with 180 degree symmetry, i.e., rotations by 180 degrees cannot be distinguished. It is easily implemented using standard numerical techniques and suitable for real-time applications. The presented approach is extensible to quaternions, which allow tracking arbitrary three-dimensional orientations. We evaluate our filter in a challenging scenario and compare it to a traditional Kalman filtering approach.