A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modelling

📖[IEEE Sensors Journal (JSEN) ] SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions (integrated deep learning features)

A tightly coupled and real time LiDAR-Inertial SLAM algorithm. Based upon LIMO-Velo and FAST_LIO projects.

FLVIS: Feedback Loop Based Visual Inertial SLAM

[IROS 2025] A Robust Tightly-Coupled RGBD-Inertial and Legged Odometry Fusion SLAM for Dynamic Legged Robotics

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

HybVIO visual-inertial odometry and SLAM system

DynaVINS : A Visual-Inertial SLAM for Dynamic Environments

Tightly-coupled Direct LiDAR-Inertial Odometry and Mapping Based on Cartographer3D.

Implementation of Multi-State Constraint Kalman Filter (MSCKF) for Vision-aided Inertial Navigation. This is my master's thesis.

Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

Deep Learning for Visual-Inertial Odometry