14 citations · 20 across the 5 of their papers we have counts for
6 papers
Does Online Gravity Estimation Matter? Revisiting a Silent Design Split in LiDAR-Inertial Odometry
Jie Xu, Ziyi Jin, Kangjin Yu +5
LiDAR-inertial odometry (LIO) systems differ in whether they continue estimating gravity after initialization. We compare four gravity-bias state configurations in each of FAST-LIO…
Dynamic Initialization for LiDAR-inertial SLAM
Jie Xu, Yongxin Ma, Yixuan Li +4
The accuracy of the initial state, including initial velocity, gravity direction, and IMU biases, is critical for the initialization of LiDAR-inertial SLAM systems. Inaccurate init…
MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments
Yongxin Ma, Jie Xu, Shenghai Yuan +4
SLAM plays a crucial role in automation tasks, such as warehouse logistics, healthcare robotics, and restaurant delivery. These scenes come with various challenges, including navig…
Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM
Jie Xu, Guanyu Huang, Wenlu Yu +5
Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM me…
Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process
Shenghai Yuan, Boyang Lou, Thien-Minh Nguyen +7
Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seap…
I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry
Wenlu Yu, Jie Xu, Chengwei Zhao +5
LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization method…