A New Perspective and Extension of the Gaussian Filter


Conference Paper


The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependencies in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end we view the GF from a variational-inference perspective, and analyze how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.

Author(s): Wüthrich, M. and Trimpe, S. and Kappler, D. and Schaal, S.
Book Title: Robotics: Science and Systems
Year: 2015

Department(s): Autonomous Motion
Research Project(s): Gaussian Filtering as Variational Inference
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Cross Ref: p10622

Links: Web


  title = {A New Perspective and Extension of the Gaussian Filter},
  author = {W{\"u}thrich, M. and Trimpe, S. and Kappler, D. and Schaal, S.},
  booktitle = {Robotics: Science and Systems},
  year = {2015},
  crossref = {p10622}