<div align="center"> <a href="http://erl.ucsd.edu/"> <img align="left" src="docs/media/erl.png" width="80" alt="erl"> </a> <a href="https://contextualrobotics.ucsd.edu/"> <img align="center" src="docs/media/cri.png" width="150" alt="cri"> </a> <a href="https://ucsd.edu/"> <img align="right" src="docs/media/ucsd.png" width="260" alt="ucsd"> </a> </div>

Semantic Shannon Mutual Information (SSMI)

SSMI is a library for autonomous robot exploration using a stream of depth and semantic segmentation images as the input to build a semantically annotated OctoMap in real-time.

SSMI is implemented as two ROS packages, which can be built on x86-64 and ARM-based processors:

• Semantic OctoMap implementation for building probabilistic multi-class maps of an environment (SSMI-Mapping)
• Autonomous exploration using Semantic Shannon Mutual Information (SSMI-Planning)

<p align="center"> <a href="https://www.youtube.com/embed/JOCRIObL8eQ"> <img src="docs/media/1.png" alt="SSMI"> </a> </p>

Gazebo Demonstration

Please check https://github.com/ExistentialRobotics/SSMI-Example for a Gazebo demo of SSMI.

Citation

If you found this work useful, we would appreciate if you could cite our work:

• [1] A. Asgharivaskasi, N. Atanasov. Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observations. IEEE Int. Conf. on Robotics and Automation (ICRA), 2021.

@InProceedings{Asgharivaskasi-ICRA21,
  author={Asgharivaskasi, Arash and Atanasov, Nikolay},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observations}, 
  year={2021},
  pages={1-7}

• [2] A. Asgharivaskasi, N. Atanasov. Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration. IEEE Transactions on Robotics (TRO), 2023.

@article{asgharivaskasi2023semantic,
 title={Semantic octree mapping and {S}hannon mutual information computation for robot exploration},
 author={Asgharivaskasi, Arash and Atanasov, Nikolay},
 journal={IEEE Transactions on Robotics},
 year={2023},
 publisher={IEEE}
}

Acknowledgments

We gratefully acknowledge support from ARL DCIST CRA W911NF-17-2-0181 and NSF FRR CAREER 2045945.

License

BSD License