Notes on Kalman Filter (KF, EKF, ESKF, IEKF, IESKF)
<|reference_start|>Notes on Kalman Filter (KF, EKF, ESKF, IEKF, IESKF): The Kalman Filter (KF) is a powerful mathematical tool widely used for state estimation in various domains, including Simultaneous Localization and Mapping (SLAM). This paper presents an in-depth introduction to the Kalman Filter and explores its several extensions: the Extended Kalman Filter (EKF), the Error-State Kalman Filter (ESKF), the Iterated Extended Kalman Filter (IEKF), and the Iterated Error-State Kalman Filter (IESKF). Each variant is meticulously examined, with detailed derivations of their mathematical formulations and discussions on their respective advantages and limitations. By providing a comprehensive overview of these techniques, this paper aims to offer valuable insights into their applications in SLAM and enhance the understanding of state estimation methodologies in complex environments.<|reference_end|>
@article{im2024notes,
    title={Notes on Kalman Filter (KF, EKF, ESKF, IEKF, IESKF)},
    author={Gyubeom Im},
    journal={arXiv preprint arXiv:2406.06427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06427},
    primaryClass={cs.RO}
}