Implementation of the algorithm proposed in:
D. Nuss, S. Reuter, M. Thom, T. Yuan, G. Krehl, M. Maile, A. Gern, and K. Dietmayer. A random finite set approach for dynamic occupancy grid maps with real-time application. arXiv, abs/1605.02406, 2016.
The code demonstrates the particle filter processing of occupancy grids into DOGMas.
Run the 'run_all.sh' script from 'dogma/', which contains all the necessary commands in sequence. This sequence of commands generates a simple grid example from simulation and runs the particle filter velocity estimation.
The code is written in Python 2.7 with the following pip dependencies:
Cython==0.27.3
hickle==2.1.0
idna==2.6
ipykernel==4.7.0
ipython==5.5.0
ipython-genutils==0.2.0
ipywidgets==7.0.5
jupyter==1.0.0
jupyter-client==5.1.0
jupyter-console==5.2.0
jupyter-core==4.4.0
matplotlib==1.5.3
notebook==5.2.2
numpy==1.13.3
panda==0.3.1
pathlib2==2.3.0
pbr==3.1.1
pexpect==4.0.1
pickleshare==0.7.4
Pillow==4.3.0
scikit-image==0.13.1
scikit-learn==0.19.1
scipy==1.0.0
sklearn==0.0