iG-LIO: an incremental GICP-based tightly-coupled LiDAR-inertial odometry

• Main Authors: Chen, Zijie, Xu, Yong, Yuan, Shenghai, Xie, Lihua
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2025
• 主題: Computer and Information Science; Sensor fusion; LiDAR-inertial odometry
• 在線閱讀: https://hdl.handle.net/10356/182645
• 機構: Nanyang Technological University

This work proposes an incremental Generalized Iterative Closest Point (GICP) based tightly-coupled LiDAR-inertial odometry (LIO), iG-LIO, which integrates the GICP constraints and inertial constraints into a unified estimation framework. iG-LIO uses a voxel-based surface covariance estimator to esti...