A Factor Graph-based approach to vehicle sideslip angle estimation
This provides a flexible estimation method for vehicle dynamics monitoring, though it appears incremental as it matches rather than surpasses existing performance.
The paper tackles vehicle sideslip angle estimation by proposing a factor graph-based approach as an alternative to Kalman Filter methods, achieving similar performance to state-of-the-art techniques on real vehicle datasets.
Sideslip angle is an important variable for understanding and monitoring vehicle dynamics but it lacks an inexpensive method for direct measurement. Therefore, it is typically estimated from inertial and other proprioceptive sensors onboard using filtering methods from the family of the Kalman Filter. As a novel alternative, this work proposes modelling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole dataset batch optimization for offline processing or fixed-lag smoother for on-line operation. Experimental results on real vehicle datasets validate the proposal with a good agreement between estimated and actual sideslip angle, showing similar performance than the state-of-the-art with a great potential for future extensions due to the flexible mathematical framework.