視覚支援ナビゲーションにおける可観測性・同定性・感度の解析
Observability, Identifiability and Sensitivity of Vision-Aided Navigation
視覚と慣性センサの融合による運動推定の可観測性を再検討し、センサバイアス率をノイズとみなす従来仮定を外すと不可観測になることを示した上で、感度解析により不可区別集合の体積の上界を入力特性の関数として導出した。
著者: Joshua Hernandez, Konstantine Tsotsos, Stefano Soatto
分類: cs.RO
原文アブストラクト
We analyze the observability of motion estimates from the fusion of visual and inertial sensors. Because the model contains unknown parameters, such as sensor biases, the problem is usually cast as a mixed identification/filtering, and the resulting observability analysis provides a necessary condition for any algorithm to converge to a unique point estimate. Unfortunately, most models treat sensor bias rates as noise, independent of other states including biases themselves, an assumption that is patently violated in practice. When this assumption is lifted, the resulting model is not observable, and therefore past analyses cannot be used to conclude that the set of states that are indistinguishable from the measurements is a singleton. In other words, the resulting model is not observable. We therefore re-cast the analysis as one of sensitivity: Rather than attempting to prove that the indistinguishable set is a singleton, which is not the case, we derive bounds on its volume, as a function of characteristics of the input and its sufficient excitation. This provides an explicit characterization of the indistinguishable set that can be used for analysis and validation purposes.
