https://github.com/hkust-aerial-robotics/apace
APACE: Agile and Perception-aware Trajectory Generation for Quadrotor Flights (ICRA2024)
https://github.com/hkust-aerial-robotics/apace
aerial-robotics autonomous-navigation motion-planning perception-aware
Last synced: 6 months ago
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APACE: Agile and Perception-aware Trajectory Generation for Quadrotor Flights (ICRA2024)
- Host: GitHub
- URL: https://github.com/hkust-aerial-robotics/apace
- Owner: HKUST-Aerial-Robotics
- License: gpl-3.0
- Created: 2024-01-02T05:49:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-28T11:32:31.000Z (over 1 year ago)
- Last Synced: 2025-04-22T14:15:05.391Z (6 months ago)
- Topics: aerial-robotics, autonomous-navigation, motion-planning, perception-aware
- Language: C++
- Homepage:
- Size: 11.6 MB
- Stars: 48
- Watchers: 9
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# APACE
This is the code repository for the paper contributed to ICRA2024:
*APACE: Agile and Perception-aware Trajectory Generation for Quadrotor Flights*.
**APACE** is a systematic, flexible and efficient framework for **A**gile and **P**erception-**A**ware traje**C**tory g**E**neration for quadrotor flights.
**APACE** empowers a quadrotor with visual sensors to maneuver in challenging environments while maintaining stable visual odometry, such as in a darkness scenario with only a few light spots.
Please cite our paper if you use this project in your research:
- [__APACE: Agile and Perception-Aware Trajectory Generation for Quadrotor Flights__](https://arxiv.org/abs/2403.08365), Xinyi Chen, Yichen Zhang, Boyu Zhou, and Shaojie Shen, 2024 IEEE International Conference on Robotics and Automation (ICRA2024).
```
@article{chen2024apace,
title={APACE: Agile and Perception-Aware Trajectory Generation for Quadrotor Flights},
author={Chen, Xinyi and Zhang, Yichen and Zhou, Boyu and Shen, Shaojie},
journal={arXiv preprint arXiv:2403.08365},
year={2024}
}
```
Please kindly star :star: this project if it helps you. Thanks for your support! :sparkling_heart:
## Getting Started
The setup commands have been tested on Ubuntu 20.04 (ROS Noetic). If you are using a different Ubuntu distribution, please modify accordingly.
* Install dependency and clone the repository
```
sudo apt install libgoogle-glog-dev libdw-dev
cd ${YOUR_WORKSPACE_PATH}/src
git clone https://github.com/HKUST-Aerial-Robotics/APACE.git
git clone https://github.com/xchencq/VINS-Fusion.git
cd ..
catkin_make
source devel/setup.bash
```
* Download the [map files](https://drive.google.com/drive/folders/1IPUBSd-ACf_wFpIpqLQSaWH9s2awQ6-f?usp=sharing) from Google Drive and put them in the `APACE/voxel_mapping/data` folder.
* Download the [pre-built AirSim simulator package](https://drive.google.com/file/d/1wUkvFrmSaT2qL5vDpH49VlWv2G_kEluU/view?usp=sharing) and the [Airsim library](https://drive.google.com/file/d/1rBClGuj98AnOjyl6V00V5X-BWr4BqbrQ/view?usp=sharing) utilized in our experiments from Google Drive. You can put them in any directory you like and remember to extract them. For example, you may put them in a folder `~/source`.
* Build the AirSim library
```
cd ~/source/AirSim_source
./setup.sh
./build.sh
```
* Move the `settings.json` file from the repository to `~/Documents/AirSim` folder and replace the original one.
* Setup the AirSim root as the directory path of the `AirSim_source` folder in the beginning of `APACE/airsim_ros_wrapper/CMakeLists.txt`.
## Run
After successful installation, you can run the APACE planner in the AirSim simulator.
* Launch RViz visualization
```
roslaunch plan_manage rviz.launch
```
* Launch pre-built AirSim simulator, e.g. under ~/source folder
```
~/source/CenterFeature/LinuxNoEditor/AirSimStreetView.sh -windowed
```
* Launch AirSim ROS wrapper
```
roslaunch airsim_ctrl ctrl_md_exploration.launch
```
* Launch VINS estimator
```
roslaunch vins vins_airsim.launch
```
* Launch APACE planner
```
roslaunch plan_manage agile_planner_airsim.launch
```