Implementation of "A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application"
Implementation of A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application
This repository is fast moving and we currently guarentee no backwards compatibility.
Particle count | Birth particle count | Grid cell count* | Time | GPU** | |
---|---|---|---|---|---|
Ours | 3 * 10⁵ | 3 * 10⁴ | 6.25 * 10⁴ | 79 ms | NVIDIA GTX 1050 Mobile |
Nuss et al. | 2 * 10⁶ | 2 * 10⁵ | 1.44 * 10⁶ | 50 ms | NVIDIA GTX 980 |
* increasing the grid cell count to 1.44 * 10⁶ increases the runtime by only ~20ms
** the NVIDIA GTX 980 is more than twice as fast as the NVIDIA GTX 1050 Mobile
You need OpenCV, OpenGL, GLFW3, GLEW, GLM and CUDA to compile and run this project. You can find the setup instructions for Ubuntu (tested for 18.04 LTS and 20.04 LTS) and Windows 10 below.
OpenCV:
sudo apt install libopencv-dev
GLFW3, GLEW, GLM:
sudo apt install libglfw3-dev libglew-dev libglm-dev
CUDA:
Have the most recent nvidia driver installed on your system, then check which CUDA version it supports
nvidia-smi -q | grep CUDA
Follow the corresponding instructions from the cuda toolkit archive
Update environment variables in your bashrc
(or equivalent rc file)
echo "\n# CUDA paths\nexport PATH=$PATH:/usr/local/cuda/bin\nexport CUDADIR=/usr/local/cuda\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64\n" >> ~/.bashrc
Reboot (if necessary, a couple of times)
Check that your system GPU driver is still working by executing nvidia-smi
To verify that your CUDA installation is working, consider compiling and running one of the examples from /usr/local/cuda/samples. In particular ones that use OpenGL+CUDA, e.g. samples/2_Graphics/Mandelbrot
.
OpenCV:
GLFW3, GLEW, GLM:
It is recommended to place all packages in a folder dogm/external
dogm/external
folder.In the end you should have a folder structure that's something like this:
dogm/external/glew-2.1.0
dogm/external/glfw-3.3
dogm/external/glm
CUDA:
Use CMake to build the project:
Use the local pipeline to configure, build and execute the project:
./local_pipeline_ubuntu.sh
The pipeline creates folder build
and compiles executables into that folder. Call the pipeline with -h
to get an overview of optional flags.
On Windows it's easiest to use cmake-gui. Use it to configure and generate the project like shown below (required variables are marked with a red dot):
Afterwards open the generated .sln
in Visual Studio 17/19 and compile it.
Contributions are welcome. Please make sure to apply clang-format
to your code, e.g. by letting local_pipeline_ubuntu.sh check and fix formatting for you. We also recommend to include formatting with clang-format in your editor/IDE.
Nuss et al. "A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application"
Dominik Nuß. "A Random Finite Set Approach for Dynamic Occupancy Grid Maps"
Homm et al. "Efficient Occupancy Grid Computation on the GPU with Lidar and Radar for Road Boundary Detection"
mitkina/dogma has also been a great reference whenever I got stuck.