This repo provides code for our paper "Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping". Please check out our project website for more details: https://thaipduong.github.io/sbkm.
Our code is tested with Ubuntu 18.04 and Python 3.7. It depends on the following Python packages: numpy 1.17.4, scipy 1.6.2, pandas 1.1.0, rtree 0.9.4, matplotlib 3.1.0, scikit-learn 0.20.4
. Newer versions of these packages might work but have not been tested.
Run python sbkm_demo.py
for a demo with Intel Research Lab dataset
Run python collision_checking_demo.py
for a demo of our collision checking methods for line segments (top) and curves (bottom).
If you find our papers/code useful for your research, please cite our work as follows.
- T. Duong, M. Yip, N. Atanasov. Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping. In Submission. 2020
@misc{duong2020autonomousbayesian,
title={Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping},
author={Duong, Thai and Yip, Michael and Atanasov, Nikolay},
url = {https://thaipduong.github.io/sbkm},
pdf = {https://arxiv.org/pdf/2009.07207.pdf}
eprint={2009.07207},
archivePrefix={arXiv},
primaryClass={cs.RO}
year={2020}
}
- T. Duong, N. Das, M. Yip, N. Atanasov. Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping. IEEE International Conference on Robotics and Automation (ICRA), 2020.
@inproceedings{duong2020autonomous,
title={Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping},
author={Duong, Thai and Das, Nikhil and Yip, Michael and Atanasov, Nikolay},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020},
url = {https://thaipduong.github.io/kernelbasedmap},
pdf = {https://arxiv.org/pdf/2002.01921.pdf}
}
Our code was built on top of the original fast Relevance Vector Machine training (https://github.com/AmazaspShumik/sklearn-bayes) and the Intel Research lab dataset was borrowed from Sparse Bayesian Hilbert Map code (https://github.com/RansML/Bayesian_Hilbert_Maps)