https://github.com/Yixin-F/LiLoc
(ICRA 2025) LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph
https://github.com/Yixin-F/LiLoc
factorgraphs localization slam
Last synced: about 2 months ago
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(ICRA 2025) LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph
- Host: GitHub
- URL: https://github.com/Yixin-F/LiLoc
- Owner: Yixin-F
- License: mit
- Created: 2024-08-10T12:23:09.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-03-21T10:11:59.000Z (about 2 months ago)
- Last Synced: 2025-03-21T11:25:08.560Z (about 2 months ago)
- Topics: factorgraphs, localization, slam
- Language: C++
- Homepage:
- Size: 2.28 MB
- Stars: 218
- Watchers: 9
- Forks: 25
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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.
***
## 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](https://github.com/koide3/ndt_omp)
- [livox_ros_driver](https://github.com/Livox-SDK/livox_ros_driver)
- [better_fastlio2](https://github.com/Yixin-F/better_fastlio2) (Refer to the module of "pose initialization" in this open-source repository, the new reconstructed code is comming soon.)
### 2. Build
```bash
cd /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](https://github.com/Yixin-F/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 ?
```bash
rosservice call /liloc/save_map 0.2 1 1 # save results of the current session
```
```bash
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/](https://robots.engin.umich.edu/nclt/)
```bash
roslaunch liloc run_nclt.launch
```
### 2. M2DGR dataset
Download M2DGR from [https://github.com/SJTU-ViSYS/M2DGR](https://github.com/SJTU-ViSYS/M2DGR)
```bash
roslaunch liloc run_m2dgr.launch
```
### 3. Our School dataset
Download the School dataset from [Google Driver](https://drive.google.com/drive/folders/14u6axUkISU5j9CxxUYEoU7Le1qneiOvA?usp=sharing)
```bash
roslaunch liloc run_lio_sam_mid360.launch
```
### 4. Our Factory dataset
Download the Factory dataset from [BaiDu Pan (passcode: g4a8)](https://pan.baidu.com/s/1AuivykDu-2djHE7Z6Sqg5Q?pwd=g4a8)
```bash
roslaunch liloc run_lio_sam_default.launch
```
## Citation
If you use any of this code, please cite our [paper](https://arxiv.org/abs/2409.10172).
```bibtex
@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](https://github.com/TixiaoShan/LIO-SAM), [liorf](https://github.com/YJZLuckyBoy/liorf) and [Block-Map-Based-Localization](https://github.com/YixFeng/Block-Map-Based-Localization).