未知の時間遅延を伴う慣性航法システムのためのガリレイ状態推定
Galilean State Estimation for Inertial Navigation Systems with Unknown Time Delay
GNSS測定の未知の時間遅延を明示的にモデル化し、ガリレイ対称性に基づく等変フィルタ(EqF)を用いて航法状態と時間遅延を同時推定する新しい手法を提案した。実機UAV実験とシミュレーションで有効性を示した。
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著者: Giulio Delama, Martin Scheiber, Yixiao Ge, Tarek Hamel, Stephan Weiss, Robert Mahony
分類: cs.RO
原文アブストラクト
Many Inertial Navigation Systems (INS) use Global Navigation Satellite System (GNSS) position as the primary measurement to drive filter performance and bound error growth. However, commercial-grade GNSS receivers introduce unknown measurement delays ranging from 50 ms to 300 ms depending on sensor quality and operating mode. Such time delays can significantly degrade INS performance unless they are explicitly compensated for. Existing algorithms commonly estimate this delay offline, run the filter concurrently with GNSS measurements using buffered Inertial Measurement Unit (IMU) data, and predict the current state by forward-integrating buffered inertial measurements via IMU preintegration. The state-of-the-art online method is an Extended Kalman Filter (EKF) that explicitly models the time delay as a state parameter, which defines the preintegration duration. This paper introduces a novel geometric framework for modeling time-delayed INS, in which Galilean symmetry is leveraged to provide a joint representation of space and time for consistent state estimation. An Equivariant Filter (EqF) is derived for the coupled estimation of navigation states and time delay. Validation is performed on two fixed-wing Uncrewed Aerial Vehicles (UAV) with GNSS time lags of 90 ms and 120 ms. The test flights last two to three minutes. Simulations further investigate delays up to 500 ms and provide a statistical comparison against the state-of-the-art EKF. Results show that the EqF preserves accuracy and consistency, while the EKF lacks consistency and its performance degrades significantly with increasing measurement delays.
