Official development and benchmarking kit for EnvoDat. To replicate all the results in the official EnvoDat project website, we strongly recommend using this repository to run our pre-trained models, train on custom datasets, run inferences on the models and perform benchmark evaluation of the SoTA SLAM algorithms.
To learn more about using EnvoDat to train, benchmark, and evaluate supervised learning models and perception algorithms, please take a look at GET_STARTED.md.
This work is licensed under a Creative Commons Attribution International 4.0 License.
If you use this work in your research, please cite our paper and dataset using the following BibTeX entry:
@INPROCEEDINGS{11127594,
author={Nwankwo, Linus and Ellensohn, Björn and Dave, Vedant and Hofer, Peter and Forstner, Jan and Villneuve, Marlene and Galler, Robert and Rueckert, Elmar},
booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
title={EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments},
year={2025},
volume={},
number={},
pages={153-160},
keywords={Simultaneous localization and mapping;Annotations;Heuristic algorithms;Supervised learning;Semantics;Benchmark testing;Cognition;Spatial databases;Robustness;Sensors},
doi={10.1109/ICRA55743.2025.11127594}}@software{envodat,
author = {Linus Nwankwo and Bjoern Ellensohn and Vedant Dave and Peter Hofer and Jan Forstner and Marlene Villneuve and Robert Galler and Elmar Rueckert},
title = {EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments},
note = {Project Website: \url{https://linusnep.github.io/EnvoDat/}},
eprint={2410.22200},
archivePrefix={arXiv},
url={https://linusnep.github.io/EnvoDat/}
}This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - No #430054590 (TRAIN).