Slope inspection under dense vegetation using LiDAR-based quadrotors

Nature Communications' 25
Wenyi Liu, Yunfan REN, Rui Guo, Vickie W. W. Kong, Anthony S. P. Hung, Fangcheng Zhu, Yixi Cai, Huajie Wu, Yuying Zou, and Fu Zhang

Overview

Paper: Nature Communications

Code: Github

Video Links: youtube, Bilibili

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@article{liu2025slope,
  title={Slope inspection under dense vegetation using LiDAR-based quadrotors},
  author={Liu, Wenyi and Ren, Yunfan and Guo, Rui and Kong, Vickie WW and Hung, Anthony SP and Zhu, Fangcheng and Cai, Yixi and Wu, Huajie and Zou, Yuying and Zhang, Fu},
  journal={Nature Communications},
  volume={16},
  number={1},
  pages={7411},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

1. About this work

Landslides pose significant threats to residents' safety and daily lives. To mitigate such risks, flexible debris-resisting barriers are constructed and regularly inspected, a task known as slope inspection. Traditional manual inspections are costly and difficult due to steep terrains and dense vegetation. Unmanned aerial vehicle (UAV) equipped with LiDAR and cameras offers high mobility, making them well-suited for slope inspections. However, existing UAV solutions lack comprehensive frameworks to handle dense vegetation, including robust localization, high-precision mapping, small and dynamic obstacle avoidance, and cluttered under-canopy navigation. To address these challenges, we develop a LiDAR-based quadrotor with a comprehensive software system. Our quadrotor features assisted obstacle avoidance, enabling it to autonomously avoid intricate obstacles while executing pilot commands. Field experiments conducted in collaboration with the Hong Kong Civil Engineering and Development Department demonstrate our quadrotor's ability to avoid small obstacles and maneuver in dense vegetation, validating its practical potential for slope inspection.

2. Prerequisited

2.1 Ubuntu and ROS

Ubuntu 18.04~20.04, ROS Installation

2.2 PCL and Eigen

PCL >= 1.6, follow PCL Installation

Eigen >= 3.3.4, follow Eigen Installation

glfw3:

sudo apt-get install libglfw3-dev libglew-dev

2.3 OSQP and OSQP-Eigen

Select the version below v0.6.3

• osqp-github
• osqp-document
• osqp-releases(below v0.6.3)
• qdldl-releases(below v0.1.7)

Install OSQP

git clone --recursive https://github.com/osqp/osqp
cd osqp
mkdir build
cd build
cmake ..
sudo make install

• osqp-eigen-github

Install OSQP-Eigen

git clone https://github.com/robotology/osqp-eigen.git
cd osqp-eigen
mkdir build
cd build
cmake ..
sudo make
sudo make install

2.4 serial

sudo apt-get install ros-noetic-serial

2.5 fmt

sudo apt-get install ros-noetic-rosfmt -y

2.6 joy

sudo apt-get install ros-noetic-joy

2.7 Other

A debug tool: backward.cpp

Installation

sudo apt-get install libdw-dev
wget https://raw.githubusercontent.com/bombela/backward-cpp/master/backward.hpp
sudo mv backward.hpp /usr/include

3 Make

mkdir -p slope_ws
cd slope_ws
git clone https://github.com/hku-mars/IPC.git
sudo mv slope_inspection src
catkin_make -DCATKIN_WHITELIST_PACKAGES="mars_base"
catkin_make -DCATKIN_WHITELIST_PACKAGES=

4 Run and test

4.1 run MARSIM simulator

static environment

source devel/setup.bash
roslaunch test_interface map.launch

dynamic environment

source devel/setup.bash
roslaunch test_interface map_dyn.launch

4.2 run ipc

for static environment (high traversability)

source devel/setup.bash
roslaunch ipc ipc_sim.launch

for dynamic environment (low traversability)

source devel/setup.bash
roslaunch ipc ipc_sim_dyn.launch

4.3 run rc

option 1: PX4 flight controller + receiver + remote controller

roslauch mavros px4.launch

option 2: sbus to USB module + receiver + remote controller

rosrun joy_rc subs_rc_node

purchase: sbus to USB module

option 3: joystick

roslaunch joy_rc BT_x1.launch

4.4 rc operation process

Mode	Description	Channel 4	Channel 5	Channel 10
Manual	Initial	< 1800	< 1500	< 1500
Hover	Keep hovering	> 1800	< 1500	< 1500
Pilot	Position control (without assisted obstacle avoidance)	> 1800	> 1500	< 1500
AutoPilot	Position control (with assisted obstacle avoidance)	> 1800	> 1500	trigger > 1500

Note: When switching mode, keep Channel 2 near the center position of 1500.

Note: Channel 11 for rc gain, trigger to high position to increase flight speed.

4.5 some suggestions

Due to the limited FOV of the LiDAR, the number of Unknown grid cells may reduce the traversability to navigate during assisted obstacle avoidance flight. In such cases, the pilot can move the control stick rapidly in multiple directions (forward, backward, left, and right) to convert as many Unknown grid cells as possible into Known Free state.