改进多状态约束卡尔曼滤波的实时更新策略，提升定位精度
An Immediate Update Strategy of Multi-State Constraint Kalman Filter
- 采用实时重建特征点实现即时更新
- 相比延迟更新，显著提升观测约束与滤波次数
- 适合低特征数场景下的高精度定位系统
轻量级多状态约束卡尔曼滤波（MSCKF）以高效著称，传统上采用延迟更新。本文研究基于及时重建3D特征点与测量约束的即时更新策略。理论分析表明，即时更新能构建更多观测约束并执行更多滤波更新，改善测量模型的线性化点，从而提升估计精度。数值仿真与实验均显示，即使在少量特征观测条件下，即时更新策略仍能显著增强MSCKF性能。
原文摘要 · Abstract (English)
The lightweight Multi-state Constraint Kalman Filter (MSCKF) has been well-known for its high efficiency, in which the delayed update has been usually adopted since its proposal. This work investigates the immediate update strategy of MSCKF based on timely reconstructed 3D feature points and measurement constraints. The differences between the delayed update and the immediate update are theoretically analyzed in detail. It is found that the immediate update helps construct more observation constraints and employ more filtering updates than the delayed update, which improves the linearization point of the measurement model and therefore enhances the estimation accuracy. Numerical simulations and experiments show that the immediate update strategy significantly enhances MSCKF even with a small amount of feature observations.
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