LiLoc

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This repository contains the source code for our ICRA2025 paper. In this work, we propose a versatile graph-based lifelong localization framework, LiLoc , 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:

• A graph-based framework for long-term localization featuring a flexible mode-switching mechanism, to achieve accurate multi-session localization.
• An adaptive submap joining strategy to dynamically manage (i.e., generate, select and update) prior submaps, reducing system memory consumption while ensuring the timeliness of prior knowledge.
• An egocentric factor graph (EFG) module to tightly couple multi-modal constrains, along with a novel propagation model to enhance prior constrains by distributing weighted scan matching factors in joint factor-graph optimization (JFGO).
• We achieve the competitive performance on public and custom datasets and the proposed system will be released for community use.

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Installation

1. Prerequisites

1.1 System and third-party packages

• Ubuntu $\geq$ 18.04 (tested on Noetic)

• PCL $\geq$ 1.8 (tested on PCL 1.10)

• Eigen >= 3.3.4 (default for Ubuntu 20.04)

• OpenCV $\geq$ 4.0 (tested on OpenCV 4.2)

• GTSAM $\geq$ 4.0.3 (tested on GTSAM 4.2(a))

1.2 Other Packages

• ndt_omp
• livox_ros_driver
• better_fastlio2 (Refer to the module of "pose initialization" in this open-source repository, the new reconstructed code is comming soon.)

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

Remark 1: 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.

Remark 2: 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.

Remark 3: 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.