TY - JOUR AU - Huai, Jianzhu AU - Lin, Yukai AU - Zhuang, Yuan AU - Shi, Min PY - 2021/05/18 Y2 - 2024/03/29 TI - Consistent Right-Invariant Fixed-Lag Smoother with Application to Visual Inertial SLAM JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 7 SE - AAAI Technical Track on Intelligent Robots DO - 10.1609/aaai.v35i7.16758 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16758 SP - 6084-6092 AB - State estimation problems without absolute position measurements routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, etc., whose proper operation relies on accurate state estimates and reliable covariances.Unaware of absolute positions, these problems have immanent unobservable directions.Traditional causal estimators, however, usually gain spurious information on the unobservable directions, leading to over-confident covariance inconsistent with actual estimator errors.The consistency problem of fixed-lag smoothers (FLSs) has only been attacked by the first estimate Jacobian (FEJ) technique because of the complexity to analyze their observability property.But the FEJ has several drawbacks hampering its wide adoption.To ensure the consistency of a FLS, this paper introduces the right invarianterror formulation into the FLS framework. To our knowledge, we are the first to analyze the observability of a FLS with the right invariant error.Our main contributions are twofold.As the first novelty, to bypass the complexity of analysis with the classic observability matrix, we show that observability analysis of FLSs can be done equivalently on the linearized system.Second, we prove that the inconsistency issue in the traditional FLS can beelegantly solved by the right invariant error formulation without artificially correcting Jacobians.By applying the proposed FLS to the monocular visual inertialsimultaneous localization and mapping (SLAM) problem,we confirm that the method consistentlyestimates covariance similarly to a batch smoother in simulation and that our method achieved comparable accuracy as traditional FLSs on real data. ER -