  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.
