MSCKFbased

MSCKF-based refers to methods that implement or build upon the Multi-State Constraint Kalman Filter (MSCKF) approach for visual–inertial estimation. These methods fuse data from an inertial measurement unit (IMU) and a monocular or stereo camera to estimate motion and, in some cases, a map of the environment. The defining feature of MSCKF-based systems is the use of a set of past camera poses (multi-states) within the filter, while the three-dimensional positions of observed features are not stored as part of the state.

In operation, features observed across multiple frames generate multi-frame constraints that relate several camera poses to the current state. Feature positions are eliminated through a nullspace or Schur-complement projection, allowing the filter to update pose, velocity, and IMU biases without explicit 3D landmark estimation. The IMU provides high-rate propagation between frames, and visual observations provide the update. As new frames arrive, the state may be augmented with new poses and old information is marginalized to keep computation manageable.

MSCKF-based approaches offer several advantages. They typically provide real-time performance with lower computational and memory demands than full SLAM systems, avoid explicit initialization of a dense map, and can be robust to intermittent feature observations. However, they can accumulate drift over time, depend on accurate IMU calibration and timing, and may suffer in degenerate motions (for example, pure rotation without translation) where constraints weaken.

Applications of MSCKF-based methods include robotics, drones, and handheld devices requiring reliable visual–inertial odometry. Variants differ in window size, marginalization strategy, and how they integrate with additional sensor factors, but share the core idea of using multi-state constraints to link poses across time without maintaining full landmark maps.