Advanced high-fidelity autonomy systems simulation. Carrillo, J. T., Cecil, O. M., Monroe, J. G., Trautz, A. C., Farthing, M. W., & Bray, M. D. In Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, volume 12115, pages 109–115, June, 2022. SPIE. ![Advanced high-fidelity autonomy systems simulation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The United State Army Corp of Engineers (USACE) Engineering Research and Development Center (ERDC) has developed a suite of computational tools called the Computational Test Bed (CTB) for advanced high-fidelity physics-based autonomous vehicle sensor and environment simulations. These tools provide insights into onboard navigation, image processing, sensor fusion techniques, and rapid data generation for artificial intelligence and machine learning techniques across the full spectrum (visible, NIR, MWIR, and LWIR) and for various sensor modalities (LiDAR, EO, radar). This paper presents ERDC’s CTB that allows the community to design, develop, test, and evaluate the entire autonomy space from machine learning algorithm development using augmented synthetic data to large-scale autonomous system testing.
@inproceedings{carrillo_advanced_2022,
title = {Advanced high-fidelity autonomy systems simulation},
volume = {12115},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12115/121150D/Advanced-high-fidelity-autonomy-systems-simulation/10.1117/12.2618011.full},
doi = {10.1117/12.2618011},
abstract = {The United State Army Corp of Engineers (USACE) Engineering Research and Development Center (ERDC) has developed a suite of computational tools called the Computational Test Bed (CTB) for advanced high-fidelity physics-based autonomous vehicle sensor and environment simulations. These tools provide insights into onboard navigation, image processing, sensor fusion techniques, and rapid data generation for artificial intelligence and machine learning techniques across the full spectrum (visible, NIR, MWIR, and LWIR) and for various sensor modalities (LiDAR, EO, radar). This paper presents ERDC’s CTB that allows the community to design, develop, test, and evaluate the entire autonomy space from machine learning algorithm development using augmented synthetic data to large-scale autonomous system testing.},
urldate = {2024-06-20},
booktitle = {Autonomous {Systems}: {Sensors}, {Processing} and {Security} for {Ground}, {Air}, {Sea} and {Space} {Vehicles} and {Infrastructure} 2022},
publisher = {SPIE},
author = {Carrillo, Justin T. and Cecil, Orie M. and Monroe, John G. and Trautz, Andrew C. and Farthing, Matthew W. and Bray, Matthew D.},
month = jun,
year = {2022},
pages = {109--115},
}
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