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Sideslip angle is an important variable for understanding and monitoringvehicle dynamics but it lacks an inexpensive method for direct measurement.Therefore, it is typically estimated from inertial and other proprioceptivesensors onboard using filtering methods from the family of the Kalman Filter.As a novel alternative, this work proposes modelling the problem directly as agraphical model (factor graph), which can then be optimized using a variety ofmethods, such as whole dataset batch optimization for offline processing orfixed-lag smoother for on-line operation. Experimental results on real vehicledatasets validate the proposal with a good agreement between estimated andactual sideslip angle, showing similar performance than the state-of-the-artwith a great potential for future extensions due to the flexible mathematicalframework.

A Factor Graph-based approach to vehicle sideslip angle estimation