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.

翻译：轻量级多状态约束卡尔曼滤波器（MSCKF）因其高效性而广为人知，自其提出以来通常采用延迟更新策略。本研究探讨了基于及时重建的三维特征点与测量约束的MSCKF即时更新策略。本文从理论上详细分析了延迟更新与即时更新之间的差异。研究发现，即时更新有助于构建更多的观测约束并执行更多的滤波更新，从而改善了测量模型的线性化点，进而提高了估计精度。数值仿真与实验表明，即使仅使用少量特征观测，即时更新策略也能显著提升MSCKF的性能。