SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation

Implementation code for our paper "SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation". Video demos can be found at multimedia demonstrations. A series of deep neural network-based Stochastic Cartographic Occupancy Prediction Engines (i.e. scope++, scope, and so-scope) are implemented by the Pytorch. Here are three GIFs showing the occupancy grid map prediction comparison results (0.5s, or 5 time steps into the future) of our proposed scope++, scope, and so-scope algorithms, and ConvLSTM, PhyDNet, DeepTracking, and scope-NEMC baselines on three different datasets with different robot models.

• OGM-Turtlebot2:
• OGM-Jackal:
• OGM-Spot:

Notice

Note that in our previous CoRL paper "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes"(arXiv), we used the acronym SOGMP (Stochastic Occupancy Grid Map Predictor) instead of SCOPE. The scope and scope++ algorithms were SOGMP and SOGMP++ in our previous Github repository SOGMP.

Requirements

• python 3.7
• torch 1.7.1
• tensorboard

OGM-Datasets

The related datasets can be found at: https://doi.org/10.5281/zenodo.7051560. There are three different datasets collected by three different robot models (i.e. Turtlebot2, Jackal, Spot).

• 1.OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points
• 2.OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin
• 3.OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin

Usage: so-scope (Fastest inference speed, can be deployed on resource-limited robots and combined with other learning-based algorithms)

• Download OGM-datasets from https://doi.org/10.5281/zenodo.7051560 and decompress them to the home directory:

cd ~
tar -zvxf OGM-datasets.tar.gz

• Training:

git clone https://github.com/TempleRAIL/scope.git
cd scope 
git checkout so-scope
sh run_train.sh ~/data/OGM-datasets/OGM-Turtlebot2/train ~/data/OGM-datasets/OGM-Turtlebot2/val

• Inference Demo on OGM-Turtlebot2 dataset:

git clone https://github.com/TempleRAIL/scope.git
cd scope 
git checkout so-scope
sh run_eval_demo.sh  ~/data/OGM-datasets/OGM-Turtlebot2/test

Usage: scope (The inference speed is faster than scope++)

• Download OGM-datasets from https://doi.org/10.5281/zenodo.7051560 and decompress them to the home directory:

cd ~
tar -zvxf OGM-datasets.tar.gz

• Training:

git clone https://github.com/TempleRAIL/scope.git
cd scope 
git checkout scope
sh run_train.sh ~/data/OGM-datasets/OGM-Turtlebot2/train ~/data/OGM-datasets/OGM-Turtlebot2/val

• Inference Demo on OGM-Turtlebot2 dataset:

git clone https://github.com/TempleRAIL/scope.git
cd scope 
git checkout scope
sh run_eval_demo.sh  ~/data/OGM-datasets/OGM-Turtlebot2/test

Usage: scope++ (The prediction accuracy is higher than scope)

• Download OGM-datasets from https://doi.org/10.5281/zenodo.7051560 and decompress them to the home directory:

cd ~
tar -zvxf OGM-datasets.tar.gz

• Training:

git clone https://github.com/TempleRAIL/scope.git
cd scope 
git checkout scope++
sh run_train.sh ~/data/OGM-datasets/OGM-Turtlebot2/train ~/data/OGM-datasets/OGM-Turtlebot2/val

• Inference Demo on OGM-Turtlebot2 dataset:

git clone https://github.com/TempleRAIL/scope.git
cd scope 
git checkout scope++
sh run_eval_demo.sh  ~/data/OGM-datasets/OGM-Turtlebot2/test

Citation

@article{xie2024scope,
  title={SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation},
  author={Xie, Zhanteng and Dames, Philip},
  journal={arXiv preprint arXiv:2407.00144},
  year={2024}
}

@inproceedings{xie2023sogmp,
  doi = {10.48550/ARXIV.2210.08577},
  title={Stochastic Occupancy Grid Map Prediction in Dynamic Scenes},
  author={Zhanteng Xie and Philip Dames},
  booktitle={Proceedings of The 7th Conference on Robot Learning},
  pages={1686--1705},
  year={2023},
  volume={229},
  series={Proceedings of Machine Learning Research},
  month={06--09 Nov},
  publisher={PMLR},
  url={https://proceedings.mlr.press/v229/xie23a.html}
}