Multi State Constraint Kalman Filter

The Multi-State Constraint Kalman Filter (MSCKF) is a state estimation technique primarily used for efficient and accurate visual-inertial odometry (VIO), fusing data from cameras and inertial measurement units. Current research focuses on improving MSCKF's efficiency and accuracy through novel feature tracking methods (e.g., using point and line features, or LiDAR data), reframing the filter using pose-only geometry, and incorporating additional sensor data like wheel odometry or GPS for enhanced robustness and performance in challenging environments. These advancements are significantly impacting robotics and autonomous navigation by enabling more reliable and computationally feasible localization in GPS-denied or low-feature scenarios.