occupancy_grid_mapping_torch

Introduction:

A GPU-accelerated and parallelized occupancy grid mapping algorithm that parallelizes the independent cell state update operations, written in pytorch. More details can be found in our paper "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes"(arXiv) in 7th Annual Conference on Robot Learning (CoRL) 2023.

Main code: dataset_gmapping_node.py

• Input: n timesteps lidar measurements, robot poses, velocites (calculate the required coordinate reference frame)
• Output: local occupancy grid map

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

Requirements:

• Python 3.7
• torch 1.7.1

Usage:

• 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

• Mapping: note, modify the dataset path 'pDev' in "dataset_gmapping_node.py" to your OGM-datasets storage directory

git clone https://github.com/TempleRAIL/occupancy_grid_mapping_torch.git
cd ./occupancy_grid_mapping_torch 
python dataset_gmapping_node.py

Citation

If you find this code helpful, please cite this paper:

@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={2022}
}