Tracking Multiple Features including Cross-Feature Correlations, with Observation Parameter Uncertainties

This paper presents a method for accounting for uncertainty in auxiliary observation parameters (such as observer and sensor poses and sensor calibration) in mapping and tracking estimators. The method retains joint correlations between multiple features, resulting in improved relative accuracy in feature estimates. The method is demonstrated in simulation for a bearing-only feature tracking application. The key update step is described for Gaussian forms and in a general Bayesian probabilistic form. Simulations verify the consistency and show the benefit of cross-feature correlations