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)
Please check https://github.com/ExistentialRobotics/SSMI-Example for a Gazebo demo of SSMI.
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}
}We gratefully acknowledge support from ARL DCIST CRA W911NF-17-2-0181 and NSF FRR CAREER 2045945.