Online detection of state estimator performance degradation via efficient numerical observability analysis

Abstract

An efficient observability analysis method is proposed to enable online detection of performance degradation of an optimization-based sliding window visual-inertial state estimation framework. The proposed methodology leverages numerical techniques in nonlinear observability analysis to enable online evaluation of the system observability and indication of the state estimation performance. Specifically, an empirical observability Gramian based approach is introduced to efficiently measure the observability condition of the windowed nonlinear system, and a scalar index is proposed to quantify the average system observability. The proposed approach is specialized to a challenging optimization-based sliding window monocular visual-inertial state estimation formulation and evaluated through simulation and experiments to assess the efficacy of the methodology. The analysis result shows that the proposed approach can correctly indicate degradation of the state estimation accuracy with real-time performance.