Point Cloud Denoising

input	segmentation	output
raw point-cloud	valid/clear fog rain	de-noised

Abstract

Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene understanding by causing undesired measurement points that in turn effect missing detections and false positives. In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop. In this paper, we present the first CNN-based approach to understand and filter out such adverse weather effects in point cloud data. Using a large data set obtained in controlled weather environments, we demonstrate a significant performance improvement of our method over state-of-the-art involving geometric filtering.

Download Dataset

Information: Click here for registration and download.

Dataset Information

• each channel contains a matrix with 32x400 values, ordered in layers and columns
• the coordinate system is based on the conventions for land vehicles DIN ISO 8855 (Wikipedia)

hdf5 channels	info
labels_1	groundtruth labels, 0: no label, 100: valid/clear, 101: rain, 102: fog
distance_m_1	distance in meter
intensity_1	raw intensity of the sensor
sensorX_1	x-coordinates in a projected 32x400 view
sensorY_1	y-coordinates in a projected 32x400 view
sensorZ_1	z-coordinates in a projected 32x400 view

hdf5 attributes	info
dateStr	date of the recording yyyy-mm-dd
timeStr	timestamp of the recording HH:MM:SS
meteorologicalVisibility_m	ground truth meteorological visibility in meter provided by the climate chamber
rainfallRate_mmh	ground truth rainfall rate in mm/h provided by the climate chamber

# example for reading the hdf5 attributes
import h5py
with h5py.File(filename, "r", driver='core') as hdf5:
  weather_data = dict(hdf5.attrs)

Getting Started

We provide documented tools for visualization in python using ROS. Therefore, you need to install ROS and the rospy client API first.

• install rospy

apt install python-rospy  

Then start "roscore" and "rviz" in separate terminals.

Afterwards, you can use the visualization tool:

• clone the repository:

cd ~/workspace
git clone https://github.com/rheinzler/PointCloudDeNoising.git
cd ~/workspace/PointCloudDeNoising

• create a virtual environment:

mkdir -p ~/workspace/PointCloudDeNoising/venv
virtualenv --no-site-packages -p python3 ~/workspace/PointCloudDeNoising/venv

• source virtual env and install dependencies:

source ~/workspace/PointCloudDeNoising/venv/bin/activate
pip install -r requirements.txt

• start visualization:

cd src
python visu.py

Notes:

• We used the following label mapping for a single lidar point: 0: no label, 100: valid/clear, 101: rain, 102: fog
• Before executing the script you should change the input path

Reference

If you find our work on lidar point-cloud de-noising in adverse weather useful for your research, please consider citing our work.:

@article{PointCloudDeNoising2020, 
  author   = {Heinzler, Robin and Piewak, Florian and Schindler, Philipp and Stork, Wilhelm},
  journal  = {IEEE Robotics and Automation Letters}, 
  title    = {CNN-based Lidar Point Cloud De-Noising in Adverse Weather}, 
  year     = {2020}, 
  keywords = {Semantic Scene Understanding;Visual Learning;Computer Vision for Transportation}, 
  doi      = {10.1109/LRA.2020.2972865}, 
  ISSN     = {2377-3774}
}

Acknowledgements

This work has received funding from the European Union under the H2020 ECSEL Programme as part of the DENSE project, contract number 692449. We thank Velodyne Lidar, Inc. for permission to publish this dataset.

Feedback/Questions/Error reporting

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