Tool to voxelize annotated point clouds.
- catkin
- Eigen >= 3.2
- boost >= 1.54
- QT >= 5.2
- OpenGL >= 3.3
- glow (catkin package)
On Ubuntu 16.04, most of the dependencies can be installed from the package manager:
sudo apt install git libeigen3-dev libboost-all-dev qtbase5-dev libglew-dev catkin
Additionally, make sure you have catkin-tools and the fetch verb installed:
sudo apt install python-pip
sudo pip install catkin_tools catkin_tools_fetch empy
If you do not have a catkin workspace already, create one:
cd
mkdir catkin_ws
cd catkin_ws
mkdir src
catkin init
cd src
git clone https://github.com/ros/catkin.git
Clone the repository in your catkin workspace:
cd ~/catkin_ws/src
git clone https://github.com/jbehley/voxelizer.git
Download the additional dependencies:
catkin deps fetch
Then, build the project:
catkin build voxelizer
Now the project root directory (e.g. ~/catkin_ws/src/voxelizer
) should contain a bin
directory containing the voxelizer.
In the bin
directory, just run ./voxelizer
to start the voxelizer.
In the settings.cfg
files you can change the followings options:
max scans: 500 # number of scans to load for a tile. (should be maybe 1000), but this currently very memory consuming. min range: 2.5 # minimum distance of points to consider. max range: 50.0 # maximum distance of points in the point cloud. ignore: 0,250,251,252,253,254 # label ids of labels that should be ignored when building a voxelgrid.
To generate the data by iterating over a sequence directory, call ./gen_data
in the bin
directory.
When loading a dataset, the data must be organized as follows:
point cloud folder ├── velodyne/ -- directory containing ".bin" files with Velodyne point clouds. ├── labels/ -- label directory, will be generated if not present. ├── calib.txt -- calibration of velodyne vs. camera. needed for projection of point cloud into camera. └── poses.txt -- file containing the poses of every scan.