nerovalerius/create_semantic_kitti_dataset.md

## create_semantic_kitti_dataset.md

      
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              create_semantic_kitti_dataset.md
            
          
    Prerequisites

Tested on Ubuntu 22.04 with an Nvidia RTX 3090


Install ROS2 Humble: LINK


Set up a ROS2 workspace: LINK

  2a. prepare automatic sourcing of the ROS2 installation and workspace:
echo "source /opt/ros/humble/setup.bash" >> ~/.bashrc
echo "source /home/user/ros2_ws/install/setup.bash" >> ~/.bashrc  

where the second line must be adapted to your ros2 workspace.
Afterwards, source the bashrc file (source ~/.bashrc)
or simply close your terminal and open an new one (thus bashrc is loaded).


Clone the pointcloud merge node: LINK


Set up a Second workspace for Point Labeler and SuMa -> See Instructions at: LINK


Install Point Labeler: LINK


Install Surfel Based Mapping (SuMa): LINK
5a. One dependency of SuMa is GTSAM. On Ubuntu 22.04 there is currently no pre-built package for GTSAM.
After installing GTSAM's dependencies, build GTSAM with:
cd ~
git clone https://github.com/borglab/gtsam.git
cd gtsam
mkdir build
cd build
cmake ..
sudo make install


Install pcd2bin: LINK


Usage


Create a folder structure which meets the KITTI standard LINK:
mkdir -p ~/dataset/pointclouds
cd ~/dataset
mkdir rosbags
mkdir -p sequences/00/velodyne
cd sequences/00
mkdir labels


Record ROSBAG of your LiDAR Data. In our case, this data is made out of 5 x Livox Horizon LiDARs:

ros2 bag record <params>


Start the pointcloud merge node inside ~/dataset/pointclouds. (the pcd files are stored in the folder in which the node is started):

https://github.com/nerovalerius/pointcloud_merge_and_kitti.


Start playback of the ROSBAG within 5 seconds. Otherwise the pointcloud merge node quits.

ros2 bag play rosbag2_2022_06_21-12_56_43_0.db3 -s sqlite3 --rate 0.5.


Convert the .pcd files with the pcd2bin node and store the .bin files inside ~/dataset/sequences/00/velodyne:

ros2 run pcd2bin_kitti pcd2bin.


create a dummy calibration file inside your created sequence:
nano ~/dataset/sequences/00/calib.txt and fill it with:
P0: 1 0 0 0 0 1 0 0 0 0 1 0
P1: 1 0 0 0 0 1 0 0 0 0 1 0
P2: 1 0 0 0 0 1 0 0 0 0 1 0
P3: 1 0 0 0 0 1 0 0 0 0 1 0
Tr: 1 0 0 0 0 1 0 0 0 0 1 0


Run SuMa with:
~/catkin_ws/src/SuMa/bin/visualizer
and open the first binary pointcloud of the sequence, e.g.: ~/dataset/sequences/00/full_cloud_00001.bin
Run SuMa to get a poses.txt file for your sequence by pressing the PLAY button
and then save poses.txt to the ~/dataset/sequences/00/ folder.
SuMa creates a posegraph for your pointcloud sequence.
Each pointcloud then gets a absolute position in a world_frame,
which is used to load a complete sequence of pointclouds into the Point Labeler.
This means that a large number of pointclouds can be labelled at once.


Open ~/dataset/sequences/00/ with Point Labeler.
Run Point Labeler with:

~/catkin_ws/src/point_labeler/bin/labeler

Point Labeler generates then generates the labes in the correct semantic KITTI format.
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