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
Note
This repository is fast moving and we currently guarentee no backwards compatibility.
Performance
| 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
Requirements and Setup
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.
Ubuntu
- OpenCV: ```console sudo apt install libopencv-dev ``` - GLFW3, GLEW, GLM: ```console 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 ```console nvidia-smi -q | grep CUDA ``` - Follow the corresponding instructions from the [cuda toolkit archive](https://developer.nvidia.com/cuda-toolkit-archive) - Update environment variables in your `bashrc` (or equivalent rc file) ```console 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`.Windows 10
- OpenCV: - Download and install OpenCV Windows from [here](https://opencv.org/releases/). - GLFW3, GLEW, GLM: - It is recommended to place all packages in a folder ```dogm/external``` - Download [GLFW3 64-bit Windows binary](https://www.glfw.org/download.html), [GLEW binaries](http://glew.sourceforge.net/) and [GLM](https://github.com/g-truc/glm/releases) - Unzip all of them in the ```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: - Have the most recent nvidia driver installed on your system, then check which CUDA version it supports - Follow the corresponding instructions from the [cuda toolkit archive](https://developer.nvidia.com/cuda-toolkit-archive)How to build
Use CMake to build the project:
Ubuntu
Use the [local pipeline](/local_pipeline_ubuntu.sh) to configure, build and execute the project: ```console ./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.Windows 10
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):
Contributing
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.
References
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.