Publications
Explore the research outputs from our lab, including journal articles, conference papers, and other scholarly works. For individual publication lists, see Dr. Joshua Mangelson's Google Scholar and Dr. Brady Moon's Google Scholar.
2026
Raw BibTeX
@incollection{sh-ch3-outlier,
title = {Robustness to Incorrect Data Association and Outliers},
author = {Heng Yang and Josh Mangelson and Yun Chang and Jingnan Shi and Luca Carlone},
booktitle = {SLAM Handbook. From Localization and Mapping to Spatial Intelligence},
publisher = {Cambridge University Press},
editor = {Luca Carlone and Ayoung Kim and Timothy Barfoot and Daniel Cremers and Frank Dellaert},
year = {2026},
url={https://github.com/SLAM-Handbook-contributors/slam-handbook-public-release}
}2025
PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration
Raw BibTeX
@inproceedings{baek2025pipe,
title = {PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration},
author = {Baek*, Seungjae and Moon*, Brady and Kim*, Seungchan and Cao, Muqing and Ho, Cherie and Scherer, Sebastian and Jeon, Jeonghwan},
year = {2025}
booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
url = {https://arxiv.org/pdf/2503.07504},
video = {https://youtu.be/oZEqbCBRn-I?si=P-H09k57_BnxLHrL}
}Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements
Raw BibTeX
@article{benham2025invariant,
title={Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements},
author={Benham, Derek and Potokar, Easton and Mangelson, Joshua G},
journal={Preprint arXiv:2506.10850},
eprint={2506.10850},
url={https://arxiv.org/abs/2506.10850v1}
year={2025}
}One-Way Acoustic Signal Localization using Received Signal Strength
Raw BibTeX
@inproceedings{benham2025oneway,
title={One-Way Acoustic Signal Localization using Received Signal Strength},
author={Benham, Derek and Smith, Clayton and Hodgins, Tristan and Palacios, Ashton and Lundrigan, Phil and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Great Lakes, Chicago, IL, USA},
year={2025},
url={https://drbenham.website/assets/2025_OCEANS_Great_Lakes__Acoustic_Signal_Localization.pdf}
}Low-cost Multi-agent Fleet for Acoustic Cooperative Localization Research
Raw BibTeX
@inproceedings{durrant2025low,
title={Low-cost Multi-agent Fleet for Acoustic Cooperative Localization Research},
author={Durrant, Nelson and Meyers, Braden and McMurray, Matthew and Smith, Clayton and Anderson, Brighton and Hodgins, Tristan and Velasco, Kalliyan and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Great Lakes, Chicago, IL, USA},
note={3rd Place in the OCEANS Student Poster Competition},
year={2025},
url={https://arxiv.org/abs/2511.08822},
eprint={2511.08822}
}MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning
Abstract
Autonomous indoor exploration is a crucial task for mobile robots, especially in search and rescue and inspection scenarios. Existing approaches often rely on handcrafted heuristics that may not generalize well across different environments. In this work, we propose MapExRL, a novel framework that combines predicted environment context with reinforcement learning to enable efficient and adaptive indoor exploration. Our method leverages a deep neural network to predict the context of the environment, such as room types and connectivity, which is then used to inform the exploration strategy learned through reinforcement learning. We evaluate our approach in simulated indoor environments and demonstrate that MapExRL outperforms traditional exploration methods in terms of coverage efficiency and adaptability to diverse layouts. Our results suggest that incorporating predicted environment context can significantly enhance the performance of autonomous indoor exploration systems.
Raw BibTeX
@inproceedings{harutyunyan2025mapexrl,
title={MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning},
author={Narek Harutyunyan* and Brady Moon* and Seungchan Kim and Cherie Ho and Adam Hung and Sebastian Scherer},
year={2025},
booktitle={2025 International Conference on Advanced Robotics (ICAR)},
url={https://arxiv.org/abs/2503.01548}
abstract={
Autonomous indoor exploration is a crucial task for mobile robots, especially in search and rescue and inspection scenarios. Existing approaches often rely on handcrafted heuristics that may not generalize well across different environments. In this work, we propose MapExRL, a novel framework that combines predicted environment context with reinforcement learning to enable efficient and adaptive indoor exploration. Our method leverages a deep neural network to predict the context of the environment, such as room types and connectivity, which is then used to inform the exploration strategy learned through reinforcement learning. We evaluate our approach in simulated indoor environments and demonstrate that MapExRL outperforms traditional exploration methods in terms of coverage efficiency and adaptability to diverse layouts. Our results suggest that incorporating predicted environment context can significantly enhance the performance of autonomous indoor exploration systems.
},
video = {https://youtu.be/XYZ123abcde}
}MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions
Raw BibTeX
@inproceedings{ho2025mapex,
title = {MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions},
author = {Ho*, Cherie and Kim*, Seungchan and Moon, Brady and Parandekar, Aditya and Harutyunyan, Narek and Wang, Chen and Sycara, Katia and Best, Graeme and Scherer, Sebastian},
year = {2025}
doi = {10.1109/ICRA55743.2025.11128862},
selected = {true},
booktitle = {2025 IEEE International Conference on Robotics and Automation (ICRA)},
url = {https://arxiv.org/pdf/2409.15590},
code = {https://github.com/castacks/MapEx},
video = {https://youtu.be/x6vEn8d6lN0}
}Systems and methods for active-light based precision localization of aircrafts in GPS-denied environments
Raw BibTeX
@misc{hunter2025systems,
title={Systems and methods for active-light based precision localization of aircrafts in GPS-denied environments},
author={Hunter, Anthony and McLain, Timothy and Mangelson, Joshua and Long, Gary and Hopman, Pablo and Kumar, Siddhartha and Akagi, David Christopher and Velasco, Kalliyan},
year={2025},
month={January},
note={US Patent 12,205,484},
url={https://patents.google.com/patent/US12205484B1/en}
}Continuous-Time Estimation in the Flat Output Space Using B-Splines
Raw BibTeX
@inproceedings{johnson2025continuous,
title={Continuous-Time Estimation in the Flat Output Space Using B-Splines},
author={Johnson, Jacob C and Mangelson, Joshua G and Beard, Randal W},
booktitle={IEEE/ION Position, Location and Navigation Symposium (PLANS)},
pages={1439--1446},
url={https://www2.ion.org/plans/abstracts.cfm?paperID=15197},
eprint={2503.06784},
year={2025}
}Hierarchical Planning for Long-Horizon Multi-Target Tracking Under Target Motion Uncertainty
Raw BibTeX
@ARTICLE{junbin2025,
author={Yuan, Junbin and Moon, Brady and Cao, Muqing and Scherer, Sebastian},
journal={IEEE Robotics and Automation Letters},
title={Hierarchical Planning for Long-Horizon Multi-Target Tracking Under Target Motion Uncertainty},
year={2025},
month=oct,
volume={},
url={https://arxiv.org/pdf/2510.10421},
number={},
pages={1-8}
keywords={Target tracking;Uncertainty;Planning;Robots;Spirals;Robot sensing systems;Sensors;Search problems;Trajectory;Covariance matrices;Planning under Uncertainty;Task and Motion Planning;Motion and Path Planning},
doi={10.1109/LRA.2025.3625492}
}Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid
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@inproceedings{kapoor2025demonstrating,
title = {Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid},
author = {Kapoor*, Parv and Higgins*, Ian and Keetha*, Nikhil and Patrikar*, Jay and Moon, Brady and Ye, Zelin and He, Yao and Cisneros, Ivan and Hu, Yaoyu and Liu, Changliu and Kang, Eunsuk and Scherer, Sebastian},
year = {2025}
booktitle = {Robotics: Science and Systems (RSS)},
url = {https://arxiv.org/pdf/2505.03694},
video = {https://youtu.be/oQxh6X9sDtw}
}Testing and Evaluation of Underwater Vehicle Using Hardware-In-The-Loop Simulation with HoloOcean
Raw BibTeX
@inproceedings{meyers2025testing,
title={Testing and Evaluation of Underwater Vehicle Using Hardware-In-The-Loop Simulation with HoloOcean},
author={Meyers, Braden and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Great Lakes, Chicago, IL, USA},
note={1st Place in the OCEANS Student Poster Competition},
year={2025},
url={https://www.arxiv.org/abs/2511.07687},
eprint={2511.07687}
}IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning
Raw BibTeX
@inproceedings{moon2025ia-tigris,
title = {IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning},
author = {Moon, Brady and Suvarna, Nayana and Jong, Andrew and Chatterjee, Satrajit and Yuan, Junbin and Scherer, Sebastian},
year = {2025},
url = {https://arxiv.org/abs/2502.15961}
eprint = {2502.15961},
archiveprefix = {arXiv},
primaryclass = {cs.RO},
video = {https://youtu.be/etFLanBdgHs}
}Raw BibTeX
@phdthesis{moon2025informative,
title={Informative Path Planning Toward Autonomous Real-World Applications},
author={Moon, Brady},
year={2025},
school={Carnegie Mellon University},
booktitle={Ph.D. Dissertation},
url={https://www.ri.cmu.edu/app/uploads/2025/05/bradym_phd_ri_2025.pdf},
video={https://youtu.be/mWyqYeOTZIs?si=krfD6GQ9P_OuzO7Y}}Cross-Modal Stereo Sonar Geometry: Projections for Sidescan and Forward-looking Sonar
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@inproceedings{norman2025feature,
title={Cross-Modal Stereo Sonar Geometry: Projections for Sidescan and Forward-looking Sonar},
author={Norman, Kalin and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Great Lakes, Chicago, IL, USA},
year={2025}
}A Preview of HoloOcean 2.0
Raw BibTeX
@article{romrell2025preview,
title={A Preview of HoloOcean 2.0},
author={Romrell, Blake and Austin, Abigail and Meyers, Braden and Anderson, Ryan and Noh, Carter and Mangelson, Joshua G},
journal={Preprint arXiv:2510.06160},
year={2025},
url={https://arxiv.org/html/2510.06160v1}
}Terra: Hierarchical Terrain-Aware 3D Scene Graph for Task-Agnostic Outdoor Mapping
Raw BibTeX
@article{samuelson2025terra,
title={Terra: Hierarchical Terrain-Aware 3D Scene Graph for Task-Agnostic Outdoor Mapping},
author={Samuelson, Chad R and Austin, Abigail and Knoop, Seth and Romrell, Blake and Slade, Gabriel R and McLain, Timothy W and Mangelson, Joshua G},
journal={Preprint arXiv:2509.19579},
year={2025}
}Towards Terrain-Aware Task-Driven 3D Scene Graph Generation in Outdoor Environments
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@article{samuelson2025towards,
title={Towards Terrain-Aware Task-Driven 3D Scene Graph Generation in Outdoor Environments},
author={Samuelson, Chad R and McLain, Timothy W and Mangelson, Joshua G},
journal={Preprint arXiv:2506.06562},
url={https://arxiv.org/abs/2506.06562},
eprint={2506.06562},
year={2025}
}Raw BibTeX
@inproceedings{Santos2025,
title = {UniSaT: Unified-Objective Belief Model and Planner to Search for and Track Multiple Objects},
url={https://arxiv.org/abs/2405.15997},
DOI = {10.2514/6.2025-2114}
booktitle = {AIAA SCITECH 2025 Forum},
publisher = {American Institute of Aeronautics and Astronautics},
author = {Santos*, Leonardo and Moon*, Brady and Scherer, Sebastian and Van Nguyen, Hoa},
year = {2025}
}Factor-Graph-Based Passive Acoustic Navigation for Decentralized Cooperative Localization Using Bearing Elevation Depth Difference
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@article{velasco2025factor,
title={Factor-Graph-Based Passive Acoustic Navigation for Decentralized Cooperative Localization Using Bearing Elevation Depth Difference},
author={Velasco, Kalliyan and McLain, Timothy W and Mangelson, Joshua G},
journal={Preprint arXiv:2506.14690},
url={https://arxiv.org/abs/2506.14690},
eprint={2506.14690},
year={2025}
}Infinite Leagues Under the Sea: Realistic 3D Underwater Terrain Generation Augmented by Visual Foundation Models
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@inproceedings{zhang2025infinite,
title={Infinite Leagues Under the Sea: Realistic 3D Underwater Terrain Generation Augmented by Visual Foundation Models},
author={Zhang, Tianyi and Zhi, Weiming and Mangelson, Joshua G and Johnson-Roberson, Matthew},
booktitle={ICLR 2025 Workshop on Foundation Models in the Wild},
url={https://arxiv.org/abs/2503.06784},
eprint={2503.06784},
year={2025}
}2024
Robust IR-based pose estimation for precision VTOL aircraft landing in urban environments
Raw BibTeX
@inproceedings{akagi2024robust,
title={Robust IR-based pose estimation for precision VTOL aircraft landing in urban environments},
author={Akagi, David and McLain, Tim and Mangelson, Joshua},
booktitle={Proceedings of the IEEE International Conference on Unmanned Aircraft Systems (ICUAS)},
pages={1292--1300},
year={2024},
url={https://ieeexplore.ieee.org/abstract/document/10557092}
}Low-Cost Urban Localization with Magnetometer and LoRa Technology
Raw BibTeX
@inproceedings{benham2024low,
title={Low-Cost Urban Localization with Magnetometer and LoRa Technology},
author={Benham, Derek and Palacios, Ashton and Lundrigan, Philip and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Abu Dhabi, UAE},
pages={11715--11722},
year={2024},
url={https://ieeexplore.ieee.org/abstract/document/10801786}
}Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions
Abstract
Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.
Raw BibTeX
@Article{drones8110606,
AUTHOR = {Moon, Brady and Akagi, Christine and Peterson, Cameron K.},
TITLE = {Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions},
JOURNAL = {Drones},
VOLUME = {8},
YEAR = {2024},
NUMBER = {11}
ARTICLE-NUMBER = {606},
URL = {https://www.mdpi.com/2504-446X/8/11/606},
ISSN = {2504-446X},
ABSTRACT = {Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.},
DOI = {10.3390/drones8110606}
}Group-k consistent measurement set maximization via maximum clique over k-uniform hypergraphs for robust multi-robot map merging
Raw BibTeX
@article{forsgren2024group,
title={Group-k consistent measurement set maximization via maximum clique over k-uniform hypergraphs for robust multi-robot map merging},
author={Forsgren, Brendon and Kaess, Michael and Vasudevan, Ram and McLain, Timothy W and Mangelson, Joshua G},
journal={The International Journal of Robotics Research},
volume={43},
number={14},
pages={2245--2273},
year={2024},
publisher={SAGE Publications Sage UK: London, England}
}Continuous-time trajectory estimation: A comparative study between Gaussian process and spline-based approaches
Raw BibTeX
@article{johnson2024continuous,
title={Continuous-time trajectory estimation: A comparative study between Gaussian process and spline-based approaches},
author={Johnson, Jacob and Mangelson, Joshua and Barfoot, Timothy and Beard, Randal},
journal={Preprint arXiv:2402.00399},
year={2024}
}Raw BibTeX
@inproceedings{parandekar2024informative,
title = {Informative Sensor Planning for a Single-Axis Gimbaled Camera on a Fixed-Wing UAV},
author = {Parandekar, Aditya and Moon, Brady and Suvarna, Nayana and Scherer, Sebastian},
year = {2024}
booktitle = {2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
pages = {1798--1804},
doi = {10.1109/CASE59546.2024.10711697},
url = {https://arxiv.org/abs/2407.04896}
}TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations
HoloOcean: A full-featured marine robotics simulator for perception and autonomy
Raw BibTeX
@article{potokar2024holoocean,
title={HoloOcean: A full-featured marine robotics simulator for perception and autonomy},
author={Potokar, Easton and Lay, Kalliyan and Norman, Kalin and Benham, Derek and Ashford, Spencer and Peirce, Randy and Neilsen, Tracianne B and Kaess, Michael and Mangelson, Joshua G},
journal={IEEE Journal of Oceanic Engineering},
year={2024},
publisher={IEEE},
url={https://ieeexplore.ieee.org/abstract/document/10638434}
}An Introduction to the Invariant Extended Kalman Filter [Lecture Notes]
Raw BibTeX
@article{potokar2024introduction,
title={An Introduction to the Invariant Extended Kalman Filter [Lecture Notes]},
author={Potokar, Easton R and Beard, Randal W and Mangelson, Joshua G},
journal={IEEE Control Systems Magazine},
volume={44},
number={6},
pages={50--71},
year={2024},
publisher={IEEE},
url={https://ieeexplore.ieee.org/abstract/document/10752718}
}A Guided Gaussian-Dirichlet Random Field for Scientist-in-the-Loop Inference in Underwater Robotics
Raw BibTeX
@inproceedings{samuelson2024guided,
title={A Guided Gaussian-Dirichlet Random Field for Scientist-in-the-Loop Inference in Underwater Robotics},
author={Samuelson, Chad R and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Yokohama, Japan},
pages={9448--9454},
year={2024},
url={https://ieeexplore.ieee.org/document/10611290}
}Recgs: Removing water caustic with recurrent gaussian splatting
Raw BibTeX
@article{zhang2024recgs,
title={Recgs: Removing water caustic with recurrent gaussian splatting},
author={Zhang, Tianyi and Zhi, Weiming and Meyers, Braden and Durrant, Nelson and Huang, Kaining and Mangelson, Joshua and Barbalata, Corina and Johnson-Roberson, Matthew},
journal={IEEE Robotics and Automation Letters},
year={2024},
publisher={IEEE},
url={https://ieeexplore.ieee.org/abstract/document/10777046}
}2023
Raw BibTeX
@INPROCEEDINGS{airtrack,
author={Ghosh, Sourish and Patrikar, Jay and Moon, Brady and Hamidi, Milad Moghassem and Scherer, Sebastian},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
title={AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking},
year={2023},
volume={}
video={https://youtu.be/H3lL_Wjxjpw},
number={},
pages={1277-1283},
keywords={Deep learning;Measurement;Image resolution;Automation;Helicopters;Real-time systems;Object tracking},
doi={10.1109/ICRA48891.2023.10160627}
}Raw BibTeX
@inproceedings{jong2023wit-uas,
title = {WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views},
author = {Jong, Andrew and Yu, Mukai and Dhrafani, Devansh and Kailas, Siva and Moon, Brady and Sycara, Katia and Scherer, Sebastian},
year = {2023},
booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {11464--11471},
code = {https://github.com/castacks/WIT-UAS-Dataset}
doi = {10.1109/IROS55552.2023.10341683},
url = {https://arxiv.org/pdf/2312.09159},
organization = {IEEE}
}Time-Optimal Path Planning in a Constant Wind for Uncrewed Aerial Vehicles using Dubins Set Classification
Raw BibTeX
@article{moon2023time-optimal,
title = {Time-Optimal Path Planning in a Constant Wind for Uncrewed Aerial Vehicles using Dubins Set Classification},
author = {Moon*, Brady and Sachdev*, Sagar and Yuan, Junbin and Scherer, Sebastian},
year = {2023},
journal = {IEEE Robotics and Automation Letters},
publisher = {IEEE}
doi = {10.1109/LRA.2023.3333167}
code = {https://github.com/castacks/trochoids},
url = {https://arxiv.org/pdf/2306.11845.pdf},
video = {https://youtu.be/qOU5gI7JshI}
}Actag: Opti-acoustic fiducial markers for underwater localization and mapping
Raw BibTeX
@inproceedings{norman2023actag,
title={Actag: Opti-acoustic fiducial markers for underwater localization and mapping},
author={Norman, Kalin and Butterfield, Daniel and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Detroit, MI, USA},
pages={9955--9962},
year={2023},
url={https://ieeexplore.ieee.org/document/10341885}
}Raw BibTeX
@article{Sharma2023,
title = {Quantifying the Effect of Weather on Advanced Air Mobility Operations},
ISSN = {2652-8800},
url = {http://dx.doi.org/10.32866/001c.66207},
DOI = {10.32866/001c.66207},
code = {https://github.com/ashimas11/python-metar},
journal = {Findings}
publisher = {Network Design Lab - Transport Findings},
author = {Sharma, Ashima and Patrikar, Jay and Moon, Brady and Scherer, Sebastian and Samaras, Constantine},
year = {2023},
month = jan
}PyPose: A library for robot learning with physics-based optimization
Raw BibTeX
@inproceedings{wang2023pypose,
title = {PyPose: A library for robot learning with physics-based optimization},
author = {Wang, Chen and Gao, Dasong and Xu, Kuan and Geng, Junyi and Hu, Yaoyu and Qiu, Yuheng and Li, Bowen and Yang, Fan and Moon, Brady and Pandey, Abhinav and others},
year = {2023},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages = {22024--22034}
code = {https://github.com/pypose/pypose}
video={https://youtu.be/XDtUDIWuGng},
doi = {10.1109/CVPR52729.2023.02109},
url = {https://arxiv.org/pdf/2209.15428}
}Staying Connected: Improving Communication Between Underwater Vehicles Using An Autonomous Intermediary
Raw BibTeX
@inproceedings{webb2023staying,
title={Staying Connected: Improving Communication Between Underwater Vehicles Using An Autonomous Intermediary},
author={Webb, Devon M and Archibald, Christopher and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Limerick, Ireland},
pages={1--8},
year={2023},
url={https://ieeexplore.ieee.org/abstract/document/10244625}
}2022
3d reconstruction of reefs using autonomous surface vessels and an analysis of chain vs 3d rugosity measurement robustness
Raw BibTeX
@inproceedings{benham20223d,
title={3d reconstruction of reefs using autonomous surface vessels and an analysis of chain vs 3d rugosity measurement robustness},
author={Benham, Derek and Newman, Aaron and Ellis, Kalai and Gill, Richard and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Hampton Roads, VA, USA},
note={1st Place in the OCEANS Student Poster Competition},
pages={1--9},
year={2022},
organization={IEEE},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Benham22oceans.pdf}
}Group-k consistent measurement set maximization for robust outlier detection
Raw BibTeX
@inproceedings{forsgren2022group,
title={Group-k consistent measurement set maximization for robust outlier detection},
author={Forsgren, Brendon and Vasudevan, Ram and Kaess, Michael and McLain, Timothy W and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Kyoto, Japan},
pages={4849--4856},
year={2022},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Forsgren22iros.pdf}
}Continuous-time trajectory estimation for differentially flat systems
Raw BibTeX
@article{johnson2022continuous,
title={Continuous-time trajectory estimation for differentially flat systems},
author={Johnson, Jacob C and Mangelson, Joshua G and Beard, Randal W},
journal={IEEE Robotics and Automation Letters},
volume={8},
number={1},
pages={145--151},
year={2022},
publisher={IEEE},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Johnson22ral.pdf}
}Raw BibTeX
@inbook{kulkarni2022aerial,
title = {Aerial Field Robotics},
author = {Kulkarni, Mihir and Moon, Brady and Alexis, Kostas and Scherer, Sebastian},
year = {2022},
booktitle = {Encyclopedia of Robotics}
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
pages = {1--15},
doi = {10.1007/978-3-642-41610-1_221-1},
isbn = {978-3-642-41610-1},
url = {https://arxiv.org/pdf/2401.10837}
}Raw BibTeX
@inproceedings{moon2022tigris,
doi = {10.1109/IROS47612.2022.9981992},
url = {https://arxiv.org/abs/2203.12830.pdf},
author = {Brady Moon and Satrajit Chatterjee and Sebastian Scherer}
code = {https://github.com/castacks/tigris},
title = {TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2022},
video = {https://youtu.be/bMw5nUGL5GQ}
}Raw BibTeX
@inproceedings{patrikar2022predicting,
title = {Predicting Like a Pilot: Dataset and Method to Predict Socially-Aware Aircraft Trajectories in Non-Towered Terminal Airspace},
author = {Patrikar, Jay and Moon, Brady and Oh, Jean and Scherer, Sebastian},
year = {2022},
booktitle = {International Conference on Robotics and Automation (ICRA)},
doi = {10.1109/ICRA46639.2022.9811972}
url = {https://arxiv.org/pdf/2109.15158.pdf},
code = {https://github.com/castacks/trajairnet},
video = {https://youtu.be/elAQXrxB2gw}
}Holoocean: An underwater robotics simulator
Raw BibTeX
@inproceedings{potokar2022holoocean,
title={Holoocean: An underwater robotics simulator},
author={Potokar, Easton and Ashford, Spencer and Kaess, Michael and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Philadelphia, PA, USA},
pages={3040--3046},
year={2022},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Potokar22icra.pdf}
}HoloOcean: Realistic sonar simulation
Raw BibTeX
@inproceedings{potokar2022holoocean,
title={HoloOcean: Realistic sonar simulation},
author={Potokar, Easton and Lay, Kalliyan and Norman, Kalin and Benham, Derek and Neilsen, Tracianne B and Kaess, Michael and Mangelson, Joshua G},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Kyoto, Japan},
pages={8450--8456},
year={2022},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Potokar22iros.pdf}
}InCOpt: Incremental constrained optimization using the Bayes tree
Raw BibTeX
@inproceedings{qadri2022incopt,
title={InCOpt: Incremental constrained optimization using the Bayes tree},
author={Qadri, Mohamad and Sodhi, Paloma and Mangelson, Joshua G and Dellaert, Frank and Kaess, Michael},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Kyoto, Japan},
pages={6381--6388},
year={2022},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Qadri22iros.pdf}
}Resilient and modular subterranean exploration with a team of roving and flying robots
Raw BibTeX
@article{scherer2022resilient,
title={Resilient and modular subterranean exploration with a team of roving and flying robots},
author={Scherer, Sebastian and Agrawal, Vasu and Best, Graeme and Cao, Chao and Cujic, Katarina and Darnley, Ryan and DeBortoli, Robert and Dexheimer, Eric and Drozd, Bill and Garg, Rohit and others},
journal={Field Robotics},
volume={2},
pages={678--734},
year={2022},
publisher={FRPS},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Scherer22fieldrobotics.pdf}
}Shapemap 3-d: Efficient shape mapping through dense touch and vision
Raw BibTeX
@inproceedings{suresh2022shapemap,
title={Shapemap 3-d: Efficient shape mapping through dense touch and vision},
author={Suresh, Sudharshan and Si, Zilin and Mangelson, Joshua G and Yuan, Wenzhen and Kaess, Michael},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Philadelphia, PA, USA},
pages={7073--7080},
year={2022},
url={https://robots.et.byu.edu/jmangelson/pubs/2022/Shuresh22icra.pdf}
}2021
A graph-based method for joint instance segmentation of point clouds and image sequences
Raw BibTeX
@inproceedings{abello2021graph,
title={A graph-based method for joint instance segmentation of point clouds and image sequences},
author={Abello, Montiel and Mangelson, Joshua G and Kaess, Michael},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Xi'an, China},
pages={9565--9571},
year={2021},
url={https://robots.et.byu.edu/jmangelson/pubs/2021/Abello21icra.pdf}
}Raw BibTeX
@article{Akagi2021,
title = {Gesture commands for controlling high-level UAV behavior},
volume = {3},
ISSN = {2523-3971},
url = {http://dx.doi.org/10.1007/s42452-021-04583-8},
DOI = {10.1007/s42452-021-04583-8},
number = {6}
journal = {SN Applied Sciences},
publisher = {Springer Science and Business Media LLC},
author = {Akagi, John and Morris, T. Devon and Moon, Brady and Chen, Xingguang and Peterson, Cameron K.},
year = {2021},
month = may
}In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery
Raw BibTeX
@article{arodrigues2021in-flight,
title = {In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery},
author = {Rodrigues, Thiago A. and Patrikar, Jay and Choudhry, Arnav and Feldgoise, Jacob and Arcot, Vaibhav and Gahlaut, Aradhana and Lau, Sophia and Moon, Brady and Wagner, Bastian and Matthews, H. Scott and Scherer, Sebastian and Samaras, Constantine},
year = {2021},
month = jun,
journal = {Scientific Data}
publisher = {Springer Science and Business Media LLC},
doi = {10.1038/s41597-021-00930-x},
url = {https://arxiv.org/pdf/2103.13313}
}Hypermap: Compressed 3d map for monocular camera registration
Raw BibTeX
@inproceedings{chang2021hypermap,
title={Hypermap: Compressed 3d map for monocular camera registration},
author={Chang, Ming-Fang and Mangelson, Joshua and Kaess, Michael and Lucey, Simon},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Xi'an, China},
pages={11739--11745},
year={2021},
url={https://robots.et.byu.edu/jmangelson/pubs/2021/Chang21icra.pdf}
}Map compressibility assessment for lidar registration
Raw BibTeX
@inproceedings{chang2021map,
title={Map compressibility assessment for lidar registration},
author={Chang, Ming-Fang and Dong, Wei and Mangelson, Joshua and Kaess, Michael and Lucey, Simon},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Prague, Czech Republic (Virtual)},
pages={5560--5567},
year={2021},
url={https://robots.et.byu.edu/jmangelson/pubs/2021/Chang21iros.pdf}
}Raw BibTeX
@inproceedings{choudhry2021cvar-based,
title = {CVaR-Based Flight Energy Risk Assessment for Multirotor UAVs Using a Deep Energy Model},
author = {Choudhry*, Arnav and Moon*, Brady and Patrikar*, Jay and Samaras, Constantine and Scherer, Sebastian},
year = {2021},
month = may,
booktitle = {International Conference on Robotics and Automation (ICRA)},
address = {Xi'an, China}
doi = {10.1109/ICRA48506.2021.9561658},
code = {https://github.com/castacks/cvar-energy-risk-deep-model},
url = {https://arxiv.org/pdf/2105.15189},
video = {https://youtu.be/PHXGigqilOA}
}Invariant Extended Kalman Filtering for Underwater Navigation
Raw BibTeX
@article{potokar2021invariant,
title={Invariant Extended Kalman Filtering for Underwater Navigation},
author={Potokar, Easton R and Norman, Kalin and Mangelson, Joshua G},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={3},
pages={5792--5799},
year={2021},
publisher={IEEE},
url={https://robots.et.byu.edu/jmangelson/pubs/2021/Potokar2021ral.pdf}
}Tactile SLAM: Real-time inference of shape and pose from planar pushing
Raw BibTeX
@inproceedings{suresh2021tactile,
title={Tactile SLAM: Real-time inference of shape and pose from planar pushing},
author={Suresh, Sudharshan and Bauza, Maria and Yu, Kuan-Ting and Mangelson, Joshua G and Rodriguez, Alberto and Kaess, Michael},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Xi'an, China},
pages={11322--11328},
year={2021},
url={https://robots.et.byu.edu/jmangelson/pubs/2021/Suresh21icra.pdf}
}2020
Efficient Multiresolution Scrolling Grid for Stereo Vision-based MAV Obstacle Avoidance
Raw BibTeX
@inproceedings{dexheimer2020efficient,
title={Efficient Multiresolution Scrolling Grid for Stereo Vision-based MAV Obstacle Avoidance},
author={Dexheimer, Eric and Mangelson, Joshua G and Scherer, Sebastian and Kaess, Michael},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Las Vegas, NV, USA (Virtual)},
pages={4758--4765},
year={2020},
url={https://robots.et.byu.edu/jmangelson/pubs/2020/dexheimer20iros.pdf}
}Aras: Ambiguity-aware robust active slam based on multi-hypothesis state and map estimations
Raw BibTeX
@inproceedings{hsiao2020aras,
title={Aras: Ambiguity-aware robust active slam based on multi-hypothesis state and map estimations},
author={Hsiao, Ming and Mangelson, Joshua G and Suresh, Sudharshan and Debrunner, Christian and Kaess, Michael},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Las Vegas, NV, USA (Virtual)}
pages={5037--5044},
year={2020},
url={https://robots.et.byu.edu/jmangelson/pubs/2020/Hsiao20iros.pdf}
}A robust multi-stereo visual-inertial odometry pipeline
Raw BibTeX
@inproceedings{jaekel2020robust,
title={A robust multi-stereo visual-inertial odometry pipeline},
author={Jaekel, Joshua and Mangelson, Joshua G and Scherer, Sebastian and Kaess, Michael},
booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
address={Las Vegas, NV, USA (Virtual)},
pages={4623--4630},
year={2020},
url={https://robots.et.byu.edu/jmangelson/pubs/2020/jaekel20iros.pdf}
}Characterizing the uncertainty of jointly distributed poses in the lie algebra
Raw BibTeX
@article{mangelson2020characterizing,
title={Characterizing the uncertainty of jointly distributed poses in the lie algebra},
author={Mangelson, Joshua G and Ghaffari, Maani and Vasudevan, Ram and Eustice, Ryan M},
journal={IEEE Transactions on Robotics},
volume={36},
number={5},
pages={1371--1388},
year={2020},
publisher={IEEE},
url={https://robots.et.byu.edu/jmangelson/pubs/2020/mangelson2020tro.pdf}
}Wind and the City: Utilizing UAV-Based In-Situ Measurements for Estimating Urban Wind Fields
Raw BibTeX
@inproceedings{patrikar2020,
author = {Patrikar, Jay and Moon, Brady and Scherer, Sebastian},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title = {Wind and the City: Utilizing UAV-Based In-Situ Measurements for Estimating Urban Wind Fields},
year = {2020}
doi = {10.1109/IROS45743.2020.9340812},
month = {October},
pages = {1254 - 1260},
url = {https://www.ri.cmu.edu/app/uploads/2020/10/IROS2020__Camera_ready.pdf},
video = {https://youtu.be/U4XdYgSJRZM}
}Raw BibTeX
@misc{rodrigues2020data,
title = {Data Collected with Package Delivery Quadcopter Drone},
author = {Rodrigues, Thiago A. and Patrikar, Jay and Choudhry, Arnav and Feldgoise, Jacob and Arcot, Vaibhav and Gahlaut, Aradhana and Lau, Sophia and Moon, Brady and Wagner, Bastian and Matthews, H Scott and Scherer, Sebastian and Samaras, Constantine},
year = {2020}
publisher = {Carnegie Mellon University},
pages = {213876544 Bytes},
doi = {10.1184/R1/12683453.v1},
url = {https://kilthub.cmu.edu/articles/dataset/Data_Collected_with_Package_Delivery_Quadcopter_Drone/12683453},
urldate = {2021-03-28}
}ICS: Incremental constrained smoothing for state estimation
Raw BibTeX
@inproceedings{sodhi2020ics,
title={ICS: Incremental constrained smoothing for state estimation},
author={Sodhi, Paloma and Choudhury, Sanjiban and Mangelson, Joshua G and Kaess, Michael},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Paris, France},
pages={279--285},
year={2020},
url={https://robots.et.byu.edu/jmangelson/pubs/2020/sodhi20icra.pdf}
}Incremental shape and pose estimation from planar pushing using contact implicit surfaces
Raw BibTeX
@article{suresh2016incremental,
title={Incremental shape and pose estimation from planar pushing using contact implicit surfaces},
author={Suresh, Sudharshan and Mangelson, Joshua G and Kaess, Michael},
booktitle={ICRA 2020 Workshop on Closing the Perception-Action Loop with Vision and Tactile Sensing (ViTac 2020)}
year={2020},
month={May},
url={https://www.ri.cmu.edu/publications/incremental-shape-and-pose-estimation-from-planar-pushing-using-contact-implicit-surfaces/}
}Active SLAM using 3D submap saliency for underwater volumetric exploration
Raw BibTeX
@inproceedings{suresh2020active,
title={Active SLAM using 3D submap saliency for underwater volumetric exploration},
author={Suresh, Sudharshan and Sodhi, Paloma and Mangelson, Joshua G and Wettergreen, David and Kaess, Michael},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Paris, France},
pages={3132--3138},
year={2020},
url={https://robots.et.byu.edu/jmangelson/pubs/2020/suresh20icra.pdf}
}2019
Guaranteed globally optimal planar pose graph and landmark SLAM via sparse-bounded sums-of-squares programming
Raw BibTeX
@inproceedings{mangelson2019guaranteed,
title={Guaranteed globally optimal planar pose graph and landmark SLAM via sparse-bounded sums-of-squares programming},
author={Mangelson, Joshua G and Liu, Jinsun and Eustice, Ryan M and Vasudevan, Ram},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Montreal, Canada},
pages={9306--9312},
year={2019},
url={https://arxiv.org/pdf/1809.07744.pdf}
}Toward Robust Multi-Agent Autonomous Underwater Inspection with Consistency and Global Optimality Guarantees
Raw BibTeX
@article{mangelson2019toward,
title={Toward Robust Multi-Agent Autonomous Underwater Inspection with Consistency and Global Optimality Guarantees},
author={Mangelson, Joshua G},
year={2019},
url={https://deepblue.lib.umich.edu/handle/2027.42/151558}
}2018
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors
Raw BibTeX
@inproceedings{hartley2018legged,
title={Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors},
author={Hartley, Ross and Mangelson, Josh and Gan, Lu and Jadidi, Maani Ghaffari and Walls, Jeffrey M and Eustice, Ryan M and Grizzle, Jessy W},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Brisbane, Australia}
pages={1--8},
year={2018},
url={https://robots.et.byu.edu/jmangelson/pubs/2018/hartley18icra.pdf}
}Communication Constrained Trajectory Alignment For Multi-Agent Inspection via Linear Programming
Raw BibTeX
@inproceedings{mangelson2018communication,
title={Communication Constrained Trajectory Alignment For Multi-Agent Inspection via Linear Programming},
author={Mangelson, Joshua G and Vasudevan, Ram and Eustice, Ryan M},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Charleston, SC, USA},
note={1st Place in the OCEANS Student Poster Competition},
pages={1--8},
year={2018},
url={https://robots.et.byu.edu/jmangelson/pubs/2018/mangelson18oceans.pdf}
}Pairwise Consistent Measurement Set Maximization for Robust Multi-robot Map Merging
Raw BibTeX
@inproceedings{mangelson2018pairwise,
title={Pairwise Consistent Measurement Set Maximization for Robust Multi-robot Map Merging},
author={Mangelson, Joshua G and Dominic, Derrick and Eustice, Ryan M and Vasudevan, Ram},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
address={Brisbane, Australia},
pages={1--8},
year={2018},
url={https://robots.et.byu.edu/jmangelson/pubs/2018/mangelson18icra.pdf},
note={IEEE ICRA 2018 Best Paper on Multi-Robot Systems}
}
Learned Search Parameters For Cooperating Vehicles using Gaussian Process Regressions
Raw BibTeX
@INPROCEEDINGS{moon2018,
author={Moon, Brady G. and Peterson, Cameron K.},
booktitle={2018 International Conference on Unmanned Aircraft Systems (ICUAS)},
title={Learned Search Parameters For Cooperating Vehicles using Gaussian Process Regressions},
year={2018}
doi={10.1109/ICUAS.2018.8453323}
pages={493-502}
}2017
Supplementary material: legged robot state-estimation through combined kinematic and preintegrated contact factors
Raw BibTeX
@article{hartley2017supplementary,
title={Supplementary material: legged robot state-estimation through combined kinematic and preintegrated contact factors},
author={Hartley, Ross and Mangelson, Josh and Gan, Lu and Jadidi, M Ghaffari and Walls, Jeffrey M and Eustice, Ryan M and Grizzle, Jessy W},
journal={University of Michigan, Tech. Rep.},
year={2017},
url={https://ganlumomo.github.io/assets/pdf/ICRAsupplement2018MRH.pdf}
}2016
Robust visual fiducials for skin-to-skin relative ship pose estimation
Raw BibTeX
@inproceedings{mangelson2016robust,
title={Robust visual fiducials for skin-to-skin relative ship pose estimation},
author={Mangelson, Joshua G and Wolcott, Ryan W and Ozog, Paul and Eustice, Ryan M},
booktitle={Proceedings of the IEEE OES/MTS OCEANS Conference and Exhibition},
address={Monterey, CA, USA},
pages={1--8},
year={2016},
url={https://robots.et.byu.edu/jmangelson/pubs/2016/mangelson16oceans.pdf}
}