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Repository Summary
| Description | |
| Checkout URI | https://github.com/ei-nav/light-map-navigation.git |
| VCS Type | git |
| VCS Version | main |
| Last Updated | 2025-02-14 |
| Dev Status | UNKNOWN |
| CI status | No Continuous Integration |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
| Name | Version |
|---|---|
| building_entrance_recognition | 0.0.0 |
| icp_registration | 0.0.0 |
| classic_nav_bringup | 0.0.0 |
| classic_navigation | 0.0.0 |
| imu_complementary_filter | 2.1.3 |
| ipm_image_node | 0.0.0 |
| ipm_interfaces | 0.0.0 |
| ipm_library | 0.0.0 |
| ipm_service | 0.0.0 |
| linefit_ground_segmentation | 0.0.1 |
| linefit_ground_segmentation_ros | 0.0.1 |
| pointcloud_to_laserscan | 2.0.1 |
| custom_interfaces | 0.2.0 |
| delivery_bringup | 0.0.0 |
| delivery_executor | 0.0.0 |
| entrance_exploration | 0.0.0 |
| gazebo_simulation | 1.0.0 |
| ros2_livox_simulation | 0.0.0 |
| grounded_sam2 | 0.0.0 |
| llm_delivery | 0.0.0 |
| replanner | 0.0.0 |
| task_planning | 0.0.0 |
| utils_pkg | 0.0.0 |
README
OPEN: Openstreetmap-enhanced oPen-air sEmantic Navigation
OPEN is a system designed for efficient last-mile delivery using autonomous robots. It leverages OpenStreetMap (OSM) for scalable map representation, Large Language Models (LLMs) for understanding delivery instructions, and Vision-Language Models (VLMs) for localization, map updates, and house number recognition.
Installation
The current development environment is Ubuntu 22.04, ROS2 Humble, and Gazebo Classic 11.
Using Docker
This project includes a basic Dockerfile and can be used with Dev Container for simulation testing and development.
- Install Docker and NVIDIA Container Toolkit and configure them on the host machine:
# Docker installation
https://docs.docker.com/engine/install/ubuntu/
wget http://fishros.com/install -O fishros (optional)
# NVIDIA Container Toolkit installation
https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
# nvidia-docker installation
sudo apt install nvidia-docker2
# Configure Docker container to use GPU
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
- Download and install the image:
# Download from Baidu Cloud
Link: https://pan.baidu.com/s/1xSDHBTYh8PTt4HNBLy7FWA?pwd=emgo
Password: emgo
# Load the image
docker load -i open.tar
- Clone the repository:
git clone https://github.com/EI-Nav/light-map-navigation.git --depth=1
git submodule init
git submodule update
cd src/classic_localization/FAST_LIO/
git submodule init
git submodule update
- Download Gazebo models and extract them to the
~/.gazebo/directory:
# Download from Baidu Cloud Drive (Choose one of the following):
# Option 1: Complete model library (includes unused models)
Link: https://pan.baidu.com/s/1Mkn4BHXaeWyvjxCMvw-gZQ
Password: pp8a
# Option 2: Minimal model library (recommended, contains only required models)
Link: https://pan.baidu.com/s/1CojOxnFrJjSLM_jpmkK_KA
Password: 49xs
# After extraction, place files in
~/.gazebo/
-
Install the
ms-vscode-remote.remote-containersextension in VSCode on the host machine. -
Open this project in VSCode, press
Ctrl+Shift+P, type and selectDev Containers: Rebuild and Reopen in Container. -
Install dependencies and compile:
# Set up your proxy
export https_proxy=http://127.0.0.1:7890 http_proxy=http://127.0.0.1:7890 all_proxy=socks5://127.0.0.1:7890
# Install dependencies
./build_dependencies.sh
# Compile
./build_project.sh
Source Installation
- Clone the repository:
git clone https://github.com/EI-Nav/light-map-navigation.git --depth=1
git submodule init
git submodule update
cd src/classic_localization/FAST_LIO/
git submodule init
git submodule update
- Install Livox SDK2:
sudo apt install cmake
git clone https://github.com/Livox-SDK/Livox-SDK2.git
cd ./Livox-SDK2/
mkdir build
cd build
cmake .. && make -j
sudo make install
- Set up the conda environment:
conda create --name dl_env python=3.10
conda activate dl_env
conda install -c conda-forge libstdcxx-ng
pip install "numpy<2.0" pandas matplotlib scikit-learn jupyterlab
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install opencv-python pycocotools matplotlib onnxruntime
pip install git+https://github.com/facebookresearch/sam2.git
pip install git+https://github.com/IDEA-Research/GroundingDINO.git
- Install dependencies for ros package:
cd light-map-navigation
rosdep install -r --from-paths src --ignore-src --rosdistro $ROS_DISTRO -y
pip3 install -r requirements.txt
-
Install other dependencies according to the build_dependencies.sh.
-
Compile:
./build.sh
Configuration
To use this repository, you’ll need to configure the following credentials:
VLM API
# /workspace/light-map-navigation/src/building_entrance_recognition/config/api_config.yaml
vlm_api:
key: "your_key"
base_url: "your_base_url"
model_name: "your_model_name"
LLM API
# /workspace/light-map-navigation/src/task_planning/config/api_config.yaml
llm_api:
key: "your_key"
base_url: "your_base_url"
model_name: "your_model_name"
GSAM2 Config
grounded_sam2_node:
ros__parameters:
gsam2:
scale: 1.0
do_sharpen: false
base_path: "/workspaces/light-map-navigation/src/grounded_sam2/grounded_sam2/grounded_sam2/"
text_prompt: "ground . car . building"
sam2_checkpoint: "./checkpoints/sam2.1_hiera_small.pt"
sam2_model_config: "configs/sam2.1/sam2.1_hiera_s.yaml"
grounding_dino_config: "grounding_dino/groundingdino/config/GroundingDINO_SwinT_OGC.py"
grounding_dino_checkpoint: "gdino_checkpoints/groundingdino_swint_ogc.pth"
box_threshold: 0.3
text_threshold: 0.25
area_threshold: 80
processing_rate: 10.0
Run
# Allow the container to access the host's X server
xhost +
Simulation Mode Example
# Run the robot's navigation system and simulation environment
ros2 launch classic_nav_bringup bringup_sim.launch.py \
world:=MEDIUM_OSM \
mode:=nav \
lio:=fastlio \
localization:=icp \
lio_rviz:=False \
nav_rviz:=True \
use_sim_time:=True
# Run delivery-related nodes
ros2 launch delivery_bringup delivery_system_sim.launch.py
# Run client to send delivery requests
ros2 run delivery_executor delivery_executor_action_client
GSAM2 Node
If you want to use Grounded-SAM-2 for open-vocabulary instance segmentation, you can run the following commands:
# Set up your proxy
export https_proxy=http://127.0.0.1:7890 http_proxy=http://127.0.0.1:7890 all_proxy=socks5://127.0.0.1:7890
# Download SAM2 checkpoints
cd src/grounded_sam2/grounded_sam2/grounded_sam2/checkpoints
bash download_ckpts.sh
# Download GroundingDINO checkpoints
cd src/grounded_sam2/grounded_sam2/grounded_sam2/gdino_checkpoints
bash download_ckpts.sh
# Run Grounded-SAM-2 server
cd /workspaces/light-map-navigation
ros2 launch grounded_sam2 gsam2_node.launch.py
Robot Model
Four-wheeled vehicle using a differential drive model.
Known Issues
- There are coordinate system issues, with the vehicle appearing below the map.
TODO
- Delete old delivery code, models, and tests.
- Improve documentation.
Acknowledgments
This project builds upon and is inspired by several excellent works:
We thank all the contributors of these open-source projects for their valuable work.
Citation
If you find our work useful for your research, please consider giving this repository a star ⭐ and citing our paper as follows:
@misc{wang2025openbenchnewbenchmarkbaseline,
title={OpenBench: A New Benchmark and Baseline for Semantic Navigation in Smart Logistics},
author={Junhui Wang and Dongjie Huo and Zehui Xu and Yongliang Shi and Yimin Yan and Yuanxin Wang and Chao Gao and Yan Qiao and Guyue Zhou},
year={2025},
eprint={2502.09238},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2502.09238},
}
CONTRIBUTING
No CONTRIBUTING.md found.
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