@inproceedings{f406926420ae4c1ba88d60042f62b0dd,
title = "VICO: Visual-Inertial Continuous-Time Odometry Based on Generalized Hermite Spline",
abstract = "Continuous-time simultaneous localization and mapping (SLAM) facilitates the seamless fusion of asynchronous and high update-rate sensors. The traditional continuous-time parameterizaton adopts the cumulative B-splines, causing the complexity in implementation and abstraction of control points. To address these issues, this paper proposes a continuous-time visual-inertial odometry (VIO) based on generalized Hermite spline. The analytical temporal derivatives and Jacobians with respect to the control points are derived, so that the VIO is further formulated as a sliding window based optimization. To validate the efficacy of the proposed method, the extensive evaluations are conducted on the TUM VI and EuRoC dataset. The results demonstrate the state-of-the-art accuracy and realtime performance. Our implementation is fully open-source at https://github.com/FALCONS-Lab/VICO.",
keywords = "Continuous-time SLAM, sensor fusion, visual-inertial odometry",
author = "Haoyu Qi and Zhen Li and Haikuo Liu and Xiangdong Liu and Fang Deng",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 37th Chinese Control and Decision Conference, CCDC 2025 ; Conference date: 16-05-2025 Through 19-05-2025",
year = "2025",
doi = "10.1109/CCDC65474.2025.11090719",
language = "English",
series = "Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3384--3390",
booktitle = "Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025",
address = "United States",
}