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

• Main Authors: Chen, Zijie, Xu, Yong, Yuan, Shenghai, Xie, Lihua
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2025
• Subjects: Computer and Information Science; Sensor fusion; LiDAR-inertial odometry
• Online Access: https://hdl.handle.net/10356/182645
• Institution: 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...