Simultaneous Estimation and Modeling of Robotic Systems with Non-Gaussian State Belief

Abstract

This paper develops a probabilistic simultaneous estimation and modeling (SEAM) framework for estimating a robot’s state and correcting its motion model parameters. This is done by incorporating model uncertainty in state prediction and correcting parameters via optimization. In the proposed technique, belief about a state being estimated is represented by arbitrary multi-dimensional non-Gaussian probability distribution functions. The approach is validated in proof-of-concept for second-order simulated systems whose models are poorly estimated. Given sufficient state observations, the proposed framework reliably reduces and usually converges model parameter error. In comparison with existing advanced estimators, robotic state estimation is enhanced under this framework when model uncertainty is high and state belief is highly unstructured and non-Gaussian. This work holds promise for challenging robotic localization, estimation, and prediction problems across many complex domains.