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.

翻译：本文提出了一种具有可证明估计误差界的鲁棒滚动时域估计方法，用于解决同步定位与地图构建问题。我们推导了充分的稳定性条件，以保证即使在直接影响系统整体可检测性的地标可见性受限的情况下，仍能确保自状态估计的鲁棒稳定性以及地标位置估计的误差有界。这是通过将自状态与地标位置的MHE更新解耦实现的，仅当满足所需的可检测性条件时才触发单个地标的更新。解耦的MHE结构还允许地标更新的并行化，从而提高了计算效率。我们讨论了关于典型SLAM传感器配置的关键假设，包括自状态可检测性和地标测量模型的Lipschitz连续性，并针对距离测量模型引入了一种简化的处理方法。仿真结果验证了所考虑方法的有效性，突显了其效能和对噪声的鲁棒性。