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<div align="center"> <h1>LiLoc</h1> <br /> <a href=https://youtu.be/Kc2azOG8TSU>🎬Youtube</a> <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span> <a href=https://www.bilibili.com/video/BV1uatkeFEWL/?vd_source=7936e3be9727382a31661ae25224c8ad>🎬Bilibili</a> <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span> <a href="https://github.com/Yixin-F/LiLoc/blob/main/README.md#Installation">🛠️Installation</a> <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span> <a href=https://arxiv.org/abs/2409.10172>📑Paper</a> <br /> <br /> </div> <img src="doc/liloc.jpg" alt="LiLoc_cover" width="800">

This repository contains the source code for our ICRA2025 paper. In this work, we propose a versatile graph-based lifelong localization framework, <strong>LiLoc</strong> , which enhances its timeliness by maintaining a single central session while improves the accuracy through multi-modal factors between the central and subsidiary sessions. The main contributions are as follows:


Installation

1. Prerequisites

1.1 System and third-party packages

1.2 Other Packages

2. Build

cd <your workspace>/src
git clone https://github.com/koide3/ndt_omp
git clone https://github.com/Livox-SDK/livox_ros_driver
git clone https://github.com/Yixin-F/LiLoc

cd ..
catkin_make
source devel/setup.bash

Run

<strong>Remark 1:</strong> How to set your localization mode ?

Since LiLoc is a graph-based localization method with a mode-switching mechanism, you need to provide the directory where your prior maps are stored and confirm your localization mode. Edit the parameter mode in config/*.yaml files to change the localization mode. If your set lio, LiLoc can be truned in to incremantal localization mode and be directly used as a SLAM algorithm. You should edit the parameter savePCDDirectory in config/*.yaml files to confirm where the results are stored. Otherwise, if you set relo, LiLoc is truned in to relocalization mode. So, you should edit the parameter savePCDDirectory in config/*.yaml files to confirm where the prior knowledge are loaded and the parameter saveSessionDirectory in config/*.yaml files to confirm where the upated central session maps are stored.

<strong>Remark 2:</strong> How to set the initial pose ?

Since our code of "pose initailization" is under reconstrucion, you can directly use the "2D pose estimation" or refer to our previous repository named better_fastlio2 to set the initial pose. The reconstructed code will be publicly aviliable as soon as possible.

<strong>Remark 3:</strong> How to save your results ?

rosservice call /liloc/save_map 0.2 1 1  # save results of the current session
rosservice call /liloc/save_session 0.2  # save results of the updated central session

1. NCLT dataset

Download NCLT from https://robots.engin.umich.edu/nclt/

roslaunch block_localization run_nclt.launch

2. M2DGR dataset

Download M2DGR from https://github.com/SJTU-ViSYS/M2DGR

roslaunch block_localization run_m2dgr.launch

3. Our School dataset

Download the School dataset from Google Driver

roslaunch block_localization run_lio_sam_mid360.launch

4. Our Factory dataset

Download the Factory dataset from Google Driver

roslaunch block_localization run_lio_sam_default.launch

Citation

If you use any of this code, please cite our paper.

@article{fang2024liloc,
  title={LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph},
  author={Fang, Yixin and Li, Yanyan and Qian, Kun and Tombari, Federico and Wang, Yue and Lee, Gim Hee},
  journal={arXiv preprint arXiv:2409.10172},
  year={2024}
}

Acknowledgements

Thanks for the open-source projects LIO-SAM, liorf and Block-Map-Based-Localization.