An Immediate Update Strategy of Multi-State Constraint Kalman Filter

TL;DR

This paper proposes an immediate update strategy for the MSCKF that reconstructs 3D features in real-time, leading to improved accuracy and more efficient filtering compared to traditional delayed update methods.

Contribution

It introduces a novel immediate update approach for MSCKF based on real-time feature reconstruction, enhancing estimation accuracy and observation constraints.

Findings

Immediate update constructs more observation constraints.

It improves the linearization point of the measurement model.

Significantly enhances MSCKF performance with few feature observations.

Abstract

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.