LiDAR Inertial Odometry and Mapping Using Learned Registration-Relevant Features

Update

• Code for feature extraction (Python) coming soon!
• Code for DLIOM (C++) will be released separately.
• Our paper is submitted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 2025.

About

This repository contains the official implementation of the feature extractor network and ros2 node proposed in paper "LiDAR Inertial Odometry and Mapping Using Learned Registration-Relevant Features". It achieves robust and efficient real-time LiDAR Inertial Odometry using a light-weight neural network based feature extractor, as opposed to previous feature-based methods that relies on hand-crafted heuristics and parameters. More detailed maps are shown in the last section.

Dependencies

Coming Soon

Prerequisite

Coming Soon

Running

Coming Soon

Generated Maps

We present detailed maps of the Northeastern University Campus and Newer College Dataset in this section.

Northeastern University Main Campus (727.50m)

Northeastern University ISEC and Columbus Garage (548.32m)

Northeastern University ISEC Bridge

Newer College Dataset

Newer College Short	Newer College Long

Newer College Mount	Newer College Park

Newer College Quad with Dynamics	Newer College Quad Hard

Newer College Quad Medium	Newer College Quad Easy

Newer College Math Easy	Newer College Math Medium

Newer College Math Hard	Newer College Cloister

Acknowledgement

We would also like to thank Alexander Estornell, Sahasrajit Anantharamakrishnan, and Yash Mewada for setting up the scout robot hardware, and Hanna Zhang, Yanlong Ma, Kenny Chen, and Nakul Joshi for help with data collection and comments.