geo analysis script for data collected by the USVs
LSA's USVs use Atlas Scientific's water quality sensor kit and water depth obtained from a Lowrance Elite 5-TI side-scanner. These data is typically saved in ROSbags and post-processed with the scripts presented in this repository.
This repository uses a three-step flow to analyse the collected data:
Requires ROS Kinetic and Ubuntu 16.04.
cd ~/ros_catkin_ws/src
git clone https://github.com/disaster-robotics-proalertas/atlas-ros.git
catkin_make -DCMAKE_BUILD_TYPE=Release
source ~/ros_catkin_ws/devel/setup.bash
Requires ROS Kinetic and Ubuntu 16.04.
sudo apt-get install python-pandas
sudo apt-get install python-geopandas
sudo apt-get install rospy_message_converter
cd ~/ros_catkin_ws/src
git clone https://github.com/disaster-robotics-proalertas/atlas-ros.git
git clone https://github.com/eurogroep/rosbag_pandas.git
catkin_make -DCMAKE_BUILD_TYPE=Release
source ~/ros_catkin_ws/devel/setup.bash
This step assumes Ubuntu 16.04 and conda. This step is ROS-independent.
conda create -n geo_env
conda activate geo_env
conda install anaconda-clean
conda install -c conda-forge folium
conda install geopandas
conda install folium
conda install matplotlib
These packages are not required but recommended.
conda install flask
conda install jupyter
./filter_rosbag.sh <source.bag> <generated.bag>
We provide one example rosbag in './data/furg-lake.bag' generated by Step 1.
python -V # must be 2.X to have ROS compatibility
python ./rosbag2geopandas.py ./data/furg-lake.bag
conda activate geo_env
python -V # must be 3.X to have plotting tools such as folium
python ./pandas2charts.py ./data/furg-lake.bag-df.pkl
firefox ./data/furg-lake.bag-folium.html & # to open the map with the USV data plotted by layers
On Firefox, you will see the following example. Select the Layers of data you want to see.
It also generates basic statistics about the data collected by the USVs
$ python pandas2charts.py ./data/furg-lake.bag-df.pkl
Condutivity DissolvedOxygen RedoxPotential Temperature pH
count 239.000000 239.000000 239.000000 239.000000 239.000000
mean 302.131380 26.523891 172.234728 27.252251 10.847092
std 5.247477 1.709656 6.145211 0.715696 0.135265
min 290.700012 20.290001 162.600006 25.677999 10.379000
25% 297.949997 26.280001 167.800003 26.721001 10.841000
50% 302.000000 27.000000 171.899994 27.344000 10.886000
75% 306.449997 27.615001 174.500000 27.905500 10.919000
max 311.299988 28.580000 191.000000 28.268999 11.003000
Author: Alexandre Amory