Implementation code for our paper "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes"(arXiv) in Conference on Robot Learning (CoRL) 2023. Two stochastic occupancy grid map (OGM) predictor algorithms (i.e. SOGMP and SOGMP++) implemented by pytorch. Video demos can be found at multimedia demonstrations. Here are three GIFs showing the occupancy grid map prediction comparison results (0.5s, or 5 time steps into the future) of our proposed SOGMP++, SOGMP algorithms, and ConvLSTM, PhyDNet, DeepTracking, and SOGMP_NEMC baselines on three different datasets with different robot models.
The related datasets can be found at OGM-datasets
There are three different datasets collected by three different robot models (i.e. Turtlebot2, Jackal, Spot).
cd ~
tar -zvxf OGM-datasets.tar.gz
git clone https://github.com/TempleRAIL/SOGMP.git
cd SOGMP
git checkout sogmp
sh run_train.sh ~/data/OGM-datasets/OGM-Turtlebot2/train ~/data/OGM-datasets/OGM-Turtlebot2/val
git clone https://github.com/TempleRAIL/SOGMP.git
cd SOGMP
git checkout sogmp
sh run_eval_demo.sh ~/data/OGM-datasets/OGM-Turtlebot2/test
cd ~
tar -zvxf OGM-datasets.tar.gz
git clone https://github.com/TempleRAIL/SOGMP.git
cd SOGMP
git checkout sogmp++
sh run_train.sh ~/data/OGM-datasets/OGM-Turtlebot2/train ~/data/OGM-datasets/OGM-Turtlebot2/val
git clone https://github.com/TempleRAIL/SOGMP.git
cd SOGMP
git checkout sogmp++
sh run_eval_demo.sh ~/data/OGM-datasets/OGM-Turtlebot2/test
@inproceedings{xie2023stochastic,
title={Stochastic Occupancy Grid Map Prediction in Dynamic Scenes},
author={Xie, Zhanteng and Dames, Philip},
booktitle={Conference on Robot Learning},
pages={1686--1705},
year={2023},
organization={PMLR}
}
@article{xie2023stochastic,
title={Stochastic Occupancy Grid Map Prediction in Dynamic Scenes},
author={Xie, Zhanteng and Dames, Philip},
journal={arXiv preprint arXiv:2210.08577},
year={2023}
}