/HE-Nav

[RA-L 2024] HE-Nav: A High-Performance and Efficient Navigation System for Aerial-Ground Robots in Cluttered Environments

Primary LanguageC++

HE-Nav: A High-performance and Energy-efficient Navigation System for Aerial-Ground Robots

We will open source the complete code after the paper is accepted

News

  • [2023/04]: The 3D model in the simulation environment can be downloaded in OneDrive.
  • [2024/04]: 🔥 We released the code of HE-Nav in the simulation environment. The pre-trained model can be downloaded at OneDrive

Installation

The code was tested with python=3.6.9, as well as pytorch=1.10.0+cu111 and torchvision=0.11.2+cu111.

Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

  1. Clone the repository locally:
 git clone https://github.com/jmwang0117/HE-Nav.git
  1. We recommend using Docker to run the project, which can reduce the burden of configuring the environment, you can find the Dockerfile in our project, and then execute the following command:
 docker build . -t skywalker_robot -f Dockerfile
  1. After the compilation is complete, use our one-click startup script in the same directory:
 bash create_container.sh

Pay attention to switch docker image

  1. Next enter the container and use git clone our project
 docker exec -it robot bash
  1. Then catkin_make compiles this project
 apt update && sudo apt-get install libarmadillo-dev ros-melodic-nlopt

Run the following commands

pip install pyyaml
pip install rospkg
pip install imageio
catkin_make
source devel/setup.bash
sh src/run.sh

You've begun this project successfully; enjoy yourself!

Dataset

  • SemanticKITTI

Acknowledgement

Many thanks to these excellent open source projects: