Rigorously Bayesian range finder sensor model for dynamic environments

This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. The modeling rigorously explains all model assumptions and parameters, improving the physical interpretation of all parameters and the intuition behind the model choices. With respect to the state of the art model [1], this paper proposes: (i) a different functional form for the probability of range measurements caused by unexpected objects, (ii) an intuitive explanation for the discontinuity encountered in the cited paper, and (iii) a reduction in the number of model parameters, while maintaining the same representational power for experimentally obtained data. The proposed beam model is called RBBM, short for rigorously Bayesian beam model. A maximum-likelihood estimation and a variational Bayesian estimation algorithm (both based on expectation-maximization) are proposed to learn the model parameters.