ROS_Raw_Kitti_Player;

Ros Package to access manipulate and process Raw KITTI dataset. In this Project there is sensor fusion, and some other perception is done. Once it is complete I will release project Manual. In current phase , once you execute this Project you should see something similiar to below picture.

Latest Commit;

Initial Version

Installing ROS

Accept software from packages.ros.org.

sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'

Set up keys

sudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 421C365BD9FF1F717815A3895523BAEEB01FA116

Debian up-to-date:

sudo apt-get update

Install full ROS version:

sudo apt-get install ros-kinetic-desktop-full

You will need to initialize rosdep:

sudo rosdep init

rosdep update

It's convenient if the ROS environment variables are automatically added to your bash session every time a new shell is launched:

echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc source ~/.bashrc

Clone and Build This Repository

cd catkin_ws/src

git clone https://github.com/jediofgever/ROS_Raw_Kitti_Player.git

cd ROS_Raw_Kitti_Player

git submodule update --init --recursive

cd .. && catkin_make

Getting Instance Segmented Images of KITTI

for the testing without installing maskrcnn and other dependencies; , I provide obtained masks for scenario; 2011_09_26_drive_0052_sync Download files for 0052 here and place it under your base directory, which should look something like this;

/home/atas/kitti_data/2011_09_26/2011_09_26_drive_0052_sync/

for a quick start using maskrcnn;

1. maskrcnn that I used bases on Pytorch, and some other helper libs. A complete guide to install maskrcnn and requirements can be found here
2. after you have maskrcnn on your system , download raw kitti images of the scenario that you would like to test. Maskrcnn provides a script to infer masks on rgb images under demo directory. I have modified that script to obtain masks in automated fashion, here is the modified script(the paths to kitti images should be changed to according to your system path)
3. the obtained masks should be on a white background, so that when we project lidar point clouds onto this image we can differantiate objects and non-objects. to write masks on a white background ; under demo directory of maskrcnn,predictor.py should be modified as this one (very small change)

Reconfigure KITTI data Path in launch file

under this directory 'ROS_Raw_Kitti_Player/kitti_ros/launch', find 'kitti_ros_node.launch' file, change the data paths according to your own envoirmment. For example my base directory for KITTI data is as follow ;

/home/atas/kitti_data/2011_09_26/2011_09_26_drive_0052_sync/

basically once you need to change username with yours , it should be fine afterwards.

Execute Kitti_ros node

cd catkin_ws

source devel/setup.bash

roslaunch kitti_ros kitti_ros_node.launch

Now RVIZ should open and you should be able to see something similar to above picture

Referencees

A Special Thanks to Simon for letting me to use his helper package which saved me bunch of time. Checkout his awesome ROS Perception project here

TODO;

Complete Documentation