Noising the point cloud.
Video for IROS 2020: https://youtu.be/fy-E4sJ-7bA
Video for ROSCon 2020: https://vimeo.com/480569545
The package is tested in Ubuntu 16.04, ROS kinetic 1.12.14, Python 3.6.
Requirements:
numpy 1.17.2
scikit-learn 0.23.1
tensorflow 1.14.0
keras 2.2.4
Anaconda3 is recommended. With Anaconda3, only tensorflow and keras need to be installed.
To make ROS and Anaconda3 compatible, in a new terminal:
gedit ~/.bashrc
add: source /opt/ros/kinetic/setup.bash
delete: export PATH="/home/tyang/anaconda3/bin:$PATH"
source ~/.bashrc
before launch the package:
export PATH="/home/tyang/anaconda3/bin:$PATH"
example:
download the lanoising package and decompress in ./src of your catkin workspace (e.g. catkin_ws).
in a new terminal:
cd ./catkin_ws
catkin_make
download the models and put all the files in ./catkin_ws/src/lanoising/models:
https://drive.google.com/file/d/1CoVrr3dVQ5DY4WpF7xCM9z6Vx7PYKW1w/view?usp=sharing
or: https://pan.baidu.com/s/1ZFhiuWFYNuSCThR02bLO8A with the code: ptio
in the terminal:
roscore
in a new terminal:
rviz
play the reference rosbag (point clouds recorded by velodyne LiDAR under clear weather conditions):
rosbag play -l --clock 2019-02-19-17-13-37.bag
in rviz, change the Fixed frame to "velodyne".
add the topic "/velodyne_points" in rviz to show the reference data.
set the visibility in lanoising.py.
in a new terminal:
cd ./catkin_ws
source devel/setup.bash
export PATH="/home/tyang/anaconda3/bin:$PATH"
roslaunch lanoising lanoising.launch
add the topic "/filtered_points" in rviz to show the noising point cloud.
If you publish work based on, or using, this code, we would appreciate citations to the following:
@artical{yt20iros,
author = {Tao Yang, You Li, Yassine Ruichek, and Zhi Yan}},
title = {LaNoising: A Data-driven Approach for 903nm ToF LiDAR Performance Modeling under Fog},
booktitle = {In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
month = {October},
year = {2020},
}