提出一种带误差边界保证的鲁棒SLAM方法，提升定位与建图稳定性。
Moving Horizon Estimation for Simultaneous Localization and Mapping with Robust Estimation Error Bounds
- 解耦自车状态与特征点更新，仅在可观测时才更新特征点
- 在可见特征点有限时仍能保证定位误差有界
- 适合传感器受限场景，如低观测率的机器人导航
本文提出一种带有可证明误差边界保证的鲁棒移动时域估计（MHE）方法，用于解决同时定位与建图（SLAM）问题。通过推导充分条件，确保在自车状态估计上具有鲁棒稳定性，并在特征点位置估计中保持误差有界，即使在特征点可视性有限、直接影响系统可观测性的条件下依然成立。该方法通过解耦自车状态与特征点位置的MHE更新，实现仅当满足可观测性条件时才进行特征点更新。解耦结构还支持特征点更新的并行化，提升计算效率。文章讨论了关键假设，包括自车状态可观测性及特征点测量模型的Lipschitz连续性，针对典型SLAM传感器配置进行了分析，并提出一种简化的距离测量模型。仿真结果验证了该方法的有效性与抗噪声鲁棒性。
原文摘要 · Abstract (English)
This paper presents a robust moving horizon estimation (MHE) approach with provable estimation error bounds for solving the simultaneous localization and mapping (SLAM) problem. We derive sufficient conditions to guarantee robust stability in ego-state estimates and bounded errors in landmark position estimates, even under limited landmark visibility which directly affects overall system detectability. This is achieved by decoupling the MHE updates for the ego-state and landmark positions, enabling individual landmark updates only when the required detectability conditions are met. The decoupled MHE structure also allows for parallelization of landmark updates, improving computational efficiency. We discuss the key assumptions, including ego-state detectability and Lipschitz continuity of the landmark measurement model, with respect to typical SLAM sensor configurations, and introduce a streamlined method for the range measurement model. Simulation results validate the considered method, highlighting its efficacy and robustness to noise.
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