• Hi!
    I'm Jingdao

    I am an Assistant Professor at Mississippi State University specializing in robotics, computer vision, and artificial intelligence. My research areas include construction robotics, disaster relief robotics, digital twins and Scan-to-BIM. My research uses self-supervised learning, class-agnostic segmentation, and incremental scene understanding techniques to achieve robust, accurate, real-time perception for intelligent, autonomous agents that operate around the built environment. I obtained my Bachelors degree at Washington University in St. Louis and my MS and PhD at Georgia Tech.

News

  • 09/2024: [Funding] from NSF ITEST
  • 07/2024: [Workshop] on deep learning for the built environment at I3CE 2024 in Pittsburgh
  • 05/2024: Established new IEEE RAS Technical Committee on Construction Robotics
  • 05/2024: [Workshop] on construction robotics at ICRA 2024 in Yokohama
  • 09/2023: [Funding] from NASA EPSCoR R3
  • 07/2023: [Funding] from IARPA WRIVA
  • 06/2023: [Funding] from NSF SaTC
  • 06/2023: [Workshop] on construction robotics at ICRA 2023 in London
  • 01/2023: 2 [papers] accepted at SPIE Defense + Commercial Sensing
  • 10/2022: 2 [papers] accepted at ECCV AI4Space workshop
  • 06/2022: [Workshop] on Scan-to-BIM at ICCEPM
  • 06/2022: [Session Chair] at ICCEPM
  • 05/2022: [Funding] from NSF CRII
  • 05/2022: [Workshop] on construction robotics at ICRA 2022 in Philadelphia
  • 04/2022: [Paper] accepted at Advanced Engineering Informatics
  • 04/2022: [Invited talk] at IEEE Mississippi
  • 05/2022: [Book Chapter] on Building Information Modeling
  • 09/2021: [Session Chair] at I3CE
  • 06/2021: [Presentation] at CVPR Workshop on Computer Vision in the Built Environment
  • 05/2021: [Paper] accepted at IEEE RA-L + presented at ICRA
  • 04/2021: [Paper] accepted at Automation in Construction
  • 06/2020: [Paper] accepted at Automation in Construction

Research

AI-driven 3D Point Cloud Modeling

Emerging technological advances in photogrammetry and LiDAR sensing allow point cloud data to be collected on a large-scale basis, yet interpreting the raw data and rendering it into formats that are useful for end users remains a fundamental research challenge. Especially, point cloud data is largely unstructured and comes with many imperfections such as sensor artifacts, occlusion, and clutter in the environment. My research work in the field of point cloud scene understanding includes 3D descriptors, multi-view segmentation, incremental segmentation, transferable point feature embeddings and learnable region growing.

Relevant papers: [1] [2] [3] [4] [5]



Scan-to-BIM Digital Twin Reconstruction

Scan-to-BIM commonly refers to the process of converting raw, unorganized laser-scanned point cloud data into interpretable, semantically-rich Building Information Models (BIM) that are easily accessible to engineers and site managers. The obtained building model can be used for construction progress monitoring, asset management, deviation detection, safety analysis, and restoration of historical buildings. My research investigates machine learning techniques for Scan-to-BIM including point cloud instance segmentation, CAD model matching and representation learning for building element retrieval.

Relevant papers: [1] [2] [3] [4] [5]

Construction Robotics

When automating the operation of heavy construction equipment such as excavators and cranes, accurate spatial information about the surrounding environment is needed to carry out manipulation tasks accurately and efficiently. My research investigates sensor-driven 3D workspace models that can be continuously updated to provide feedback concerning the progress of manipulation tasks. This work includes a workspace visualization and pose estimation framework for teleoperated excavators and a crane operation assistance system incorporating collision warning, load swing tracking, and moving hazard detection.

Relevant papers: [1] [2] [3] [4]

Disaster Relief / Radiation Mapping Robotics

Disaster relief and response plays an important role in saving lives and reducing economic loss after earthquakes, windstorm events and man-made explosions. Mobile robots represent an effective solution to assist in post-disaster reconnaissance in areas that are dangerous to human agents due to chemical or radiation leaks. My research work in this domain includes simulation of nuclear power plant disaster sites, point cloud segmentation of damaged building elements, anomaly-based crack segmentation of post-earthquake structures, and radiation mapping of nuclear facilities.

Relevant papers: [1] [2] [3] [4] [5]

Off-road Robotics

Off-road environments such as those found in construction, agriculture, forestry, mining, and rural transportation offer a significant challenge for autonomous driving due to the unstructured and unpredictable hazards in the form of uneven terrain in addition to varied vegetation and rocks. In on-road environments, it is straightforward to navigate based on cues from traffic signs and lane markings. Whereas, in off-road environments, navigation algorithms have to perform higher-level scene understanding and consider complex terrain interactions as well as the relative hazards of different obstacles in the environment. My research work in this domain include self-supervised traversability estimation, uncertainty-aware planning, and digital twin modeling for off-road environments.

Relevant papers: [1] [2]



Perception for Planetary Rovers

Planetary rovers represent one of the primary means by which humans are able to explore and understand other celestial bodies, as exemplified by the Mars 2020 rover mission and the Lunar 2023 VIPER mission. However, Mars surface images suffer from a lack of expert-labeled training data and domain shift in image features as the rover moves to different sites on a planet. In addition, due to power constraints and extensive flight qualification requirements (e.g., radiation tolerance), current space-qualified hardware still mostly relies on legacy technologies. To address these issues, this research investigates techniques such as contrastive learning, knowledge distillation, and mixed-domain training to provide more accurate and efficient perception models for planetary rovers.

Relevant papers: [1] [2] [3]

Remote Monitoring with UAVs

Highway infrastructure maintenance and monitoring tasks often involve labor-intensive activities and long inspection times. Examples of these maintenance tasks include landscaping and lawn care, detecting damaged road segments, and identifying missing road signs. To efficiently automate the maintenance inspection tasks, my research proposes an automated monitoring framework using Unmanned Aerial Vehicles (UAV). Structure from Motion (SfM) is used to create dense 3D point clouds from image data and deep learning techniques are used to segment and classify different highway assets. Point cloud-based temporal change detection is carried out with a focus on grass height estimation for monitoring highway mowing operations.

Relevant papers: [1] [2]

Publications

    2024

  1. Neupane, S., Mitra, S., Fernandez, I., Saha, S., Mittal, S., Chen, J., Pillai, S., and Rahimi, S. (2024).
    Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
    IEEE Access, vol. 12, pp. 22072-22097
    [bibtex]
    @ARTICLE{neupane2024,
    author={Neupane, Subash and Mitra, Shaswata and Fernandez, Ivan A. and Saha, Swayamjit and Mittal, Sudip and Chen, Jingdao and Pillai, Nisha and Rahimi, Shahram},
    journal={IEEE Access},
    title={Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities},
    year={2024},
    volume={12},
    number={},
    pages={22072-22097},
    doi={10.1109/ACCESS.2024.3363657},
    }
  2. Gao, K., Haverly, A., Mittal, S., Wu, J., and Chen, J. (2024).
    AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
    International Journal of Business Analytics (IJBAN), 11(1), 1-19
    [bibtex]
    @ARTICLE{gao2024,
    author={Gao, Kevin Di and Haverly, Andrew and Mittal, Sudip and Wu, Jiming and Chen, Jingdao},
    journal={International Journal of Business Analytics},
    title={AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps},
    year={2024},
    volume={11},
    number={1},
    pages={19},
    }

    3. 2023

  3. Vincent, G., Ward, I., Moore, C., Chen, J., Pak, K., Yepremyan, A., Wilson, B., and Goh, E. (2023).
    CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics
    Journal of Spacecraft and Rockets, 61:3, 728-740
    [bibtex]
    @article{vincent2023,
    author = {Vincent, Grace M. and Ward, Isaac R. and Moore, Charles and Chen, Jingdao and Pak, Kai and Yepremyan, Alice and Wilson, Brian and Goh, Edwin Y.},
    title = {CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics},
    journal = {Journal of Spacecraft and Rockets},
    volume = {61},
    number = {3},
    pages = {728-740},
    year = {2024},
    doi = {10.2514/1.A35767},
    }

    4. 2022

  4. Chen, J., and Cho, Y. (2022).
    CrackEmbed: Point feature embedding for crack segmentation from disaster site point clouds with anomaly detection
    Advanced Engineering Informatics, Volume 52, April 2022
    [bibtex]
    @article{chen2022aei,
    title = {CrackEmbed: Point feature embedding for crack segmentation from disaster site point clouds with anomaly detection},
    journal = {Advanced Engineering Informatics},
    volume = {52},
    pages = {101550},
    year = {2022},
    issn = {1474-0346},
    doi = {https://doi.org/10.1016/j.aei.2022.101550},
    url = {https://www.sciencedirect.com/science/article/pii/S1474034622000258},
    author = {Jingdao Chen and Yong Kwon Cho},
    }

    5. 2021

  5. Chen, J., Kira, Z. and Cho, Y. (2021).
    LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation
    IEEE Robotics and Automation Letters, 6(2), pp. 2799-2806. Accepted for oral presentation at the International Conference on Robotics and Automation (ICRA) 2021.
    [code] [bibtex]
    @ARTICLE{chen2021ral,
    title={LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation},
    author={J. {Chen} and Z. {Kira} and Y. K. {Cho}},
    journal={IEEE Robotics and Automation Letters},
    year = {2021},
    volume={6},
    number={2},
    pages={2799-2806},
    }
  6. Price, L., Chen, J., Park, J. and Cho Y. (2021).
    Multisensor-driven real-time crane monitoring system for blind lift operations: Lessons learned from a case study
    Automation in Construction, Volume 124, April 2021
    [bibtex]
    @article{price2021autcon,
    title = {Multisensor-driven real-time crane monitoring system for blind lift operations: Lessons learned from a case study},
    journal = {Automation in Construction},
    volume = {124},
    pages = {103552},
    year = {2021},
    issn = {0926-5805},
    doi = {https://doi.org/10.1016/j.autcon.2021.103552},
    url = {https://www.sciencedirect.com/science/article/pii/S0926580521000030},
    author = {Leon C. Price and Jingdao Chen and Jisoo Park and Yong K. Cho},
    }

    7. 2020

  7. Chen, J., Yi, J., Kahoush, M., Cho, E. and Cho, Y. (2020).
    Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting
    MDPI Sensors, 20(18), 5029
    [code] [bibtex]
    @Article{chen2020sensors,
    AUTHOR = {Chen, Jingdao and Yi, John Seon Keun and Kahoush, Mark and Cho, Erin S. and Cho, Yong K.},
    TITLE = {Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting},
    JOURNAL = {Sensors},
    VOLUME = {20},
    YEAR = {2020},
    NUMBER = {18},
    ARTICLE-NUMBER = {5029},
    URL = {https://www.mdpi.com/1424-8220/20/18/5029},
    ISSN = {1424-8220},
    DOI = {10.3390/s20185029}
    }
  8. Zeng, S., Chen, J., and Cho Y. (2020).
    User Exemplar-based Building Element Retrieval from Raw Point Clouds using Deep Point-level Features
    Automation in Construction, Volume 114, June 2020, 103159
    [bibtex]
    @article{zeng2020autcon,
    title = "User exemplar-based building element retrieval from raw point clouds using deep point-level features",
    journal = "Automation in Construction",
    volume = "114",
    pages = "103159",
    year = "2020",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2020.103159",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580519310908",
    author = "Shiqin Zeng and Jingdao Chen and Yong K. Cho",
    }
  9. Park, J.S., Chen, J., Cho, Y., Kang, D., and Son, B. (2020).
    CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning System.
    MDPI Sensors, 20(1), 34
    [code] [bibtex]
    @article{park2020sensors,
    AUTHOR = {Park, Jisoo and Chen, Jingdao and Cho, Yong K. and Kang, Dae Y. and Son, Byung J.},
    TITLE = {CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning Systems},
    JOURNAL = {Sensors},
    VOLUME = {20},
    YEAR = {2020},
    NUMBER = {1},
    ARTICLE-NUMBER = {34},
    URL = {https://www.mdpi.com/1424-8220/20/1/34},
    ISSN = {1424-8220},
    DOI = {10.3390/s20010034},
    }

    10. 2019

  10. Chen, J., Cho, Y., and Kira, Z. (2019).
    Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots.
    IEEE Robotics and Automation Letters, 4(2), pp. 1240-1246,10.1109/LRA.2019.2894915
    [code] [bibtex]
    @ARTICLE{chen2019ral,
    author={J. {Chen} and Y. K. {Cho} and Z. {Kira}},
    journal={IEEE Robotics and Automation Letters},
    title={Multi-View Incremental Segmentation of 3-D Point Clouds for Mobile Robots},
    year={2019},
    volume={4},
    number={2},
    pages={1240-1246},
    }
  11. Chen, J., Kira, Z., and Cho, Y. (2019).
    Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction.
    ASCE Journal of Computing in Civil Engineering, 33(4) DOI:10.1061/(ASCE)CP.1943-5487.0000842
    [bibtex]
    @article{chen2019jcce,
    author = {Jingdao Chen and Zsolt Kira and Yong K. Cho },
    title = {Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction},
    journal = {Journal of Computing in Civil Engineering},
    volume = {33},
    number = {4},
    pages = {04019027},
    year = {2019},
    doi = {10.1061/(ASCE)CP.1943-5487.0000842},
    }

    12. 2018

  12. Fang, Y., Chen, J., Cho, Y., and Kim, K.N. (2018).
    Vision-based Load Sway Monitoring to Improve Crane Safety in Blind Lifts.
    Journal of Structural Integrity and Maintenance, 10.1080/24705314.2018.1531348
    [bibtex]
    @article{fang2018jsim,
    author = {Yihai Fang and Jingdao Chen and Yong K. Cho and Kinam Kim and Sijie Zhang and Esau Perez},
    title = {Vision-based load sway monitoring to improve crane safety in blind lifts},
    journal = {Journal of Structural Integrity and Maintenance},
    volume = {3},
    number = {4},
    pages = {233-242},
    year = {2018},
    publisher = {Taylor & Francis},
    doi = {10.1080/24705314.2018.1531348},
    }
  13. Kim, P., Chen, J., and Cho, Y. (2018).
    SLAM-driven robotic mapping and registration of 3D point clouds.
    Automation in Construction, doi.org/10.1016/j.autcon.2018.01.009
    [bibtex]
    @article{kim2018autcon,
    title = "SLAM-driven robotic mapping and registration of 3D point clouds",
    journal = "Automation in Construction",
    volume = "89",
    pages = "38 - 48",
    year = "2018",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2018.01.009",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580517303990",
    author = "Pileun Kim and Jingdao Chen and Yong K. Cho",
    }
  14. Chen, J., Fang, Y., and Cho, Y. (2018).
    Performance Evaluation of 3D Descriptors for Object Recognition in Construction Applications.
    Automation in Construction, Volume 86,February 2018, Pages 44-52, DOI: 10.1016/j.autcon.2017.10.033
    [bibtex]
    @article{chen2018autcon,
    title = "Performance evaluation of 3D descriptors for object recognition in construction applications",
    journal = "Automation in Construction",
    volume = "86",
    pages = "44 - 52",
    year = "2018",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2017.10.033",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580517303862",
    author = "Jingdao Chen and Yihai Fang and Yong K. Cho",
    }
  15. Kim, P., Chen, J., and Cho, Y. (2018).
    Automated Point Clouds Registration using Visual and Planar Features for Construction Environments.
    ASCE Journal of Computing in Civil Engineering, Volume 32, Issue2, March 2018, DOI: 10.1061/(ASCE)CP.1943-5487.0000720
    [bibtex]
    @article{kim2018jcce,
    author = {Pileun Kim and Jingdao Chen and Yong K. Cho },
    title = {Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments},
    journal = {Journal of Computing in Civil Engineering},
    volume = {32},
    number = {2},
    pages = {04017076},
    year = {2018},
    doi = {10.1061/(ASCE)CP.1943-5487.0000720}
    }

    16. 2017

  16. Kim, P., Chen, J., and Cho, Y. (2017).
    Robotic sensing and object recognition from thermal-mapped point clouds.
    International Journal of Intelligent Robotics and Applications. September 2017, Volume 1, Issue 3, Pages 243-254, DOI: 10.1007/s41315-017-0023-9
    [bibtex]
    @Article{kim2017ijira,
    author="Kim, Pileun
    and Chen, Jingdao
    and Cho, Yong K.",
    title="Robotic sensing and object recognition from thermal-mapped point clouds",
    journal="International Journal of Intelligent Robotics and Applications",
    year="2017",
    month="Sep",
    day="01",
    volume="1",
    number="3",
    pages="243--254",
    }
  17. Chen, J., Fang, Y., and Cho, Y. (2017).
    Real-Time 3D Crane Workspace Update Using a Hybrid Visualization Approach.
    ASCE Journal of Computing in Civil Engineering, Volume 31, Issue 5, DOI: 10.1061/(ASCE)CP.1943-5487.0000698
    [bibtex]
    @article{chen2017jcce,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho },
    title = {Real-Time 3D Crane Workspace Update Using a Hybrid Visualization Approach},
    journal = {Journal of Computing in Civil Engineering},
    volume = {31},
    number = {5},
    pages = {04017049},
    year = {2017},
    doi = {10.1061/(ASCE)CP.1943-5487.0000698},
    }
  18. Park, J.W., Chen, J., and Cho, Y. (2017).
    Self-Corrective Knowledge-based Hybrid Tracking System Using BIM and Multimodal Sensors.
    Advanced Engineering Informatics, Volume 32, Issue C, April 2017, Pages 126-138, DOI: 10.1016/j.aei.2017.02.001
    [bibtex]
    @article{park2017aei,
    title = "Self-corrective knowledge-based hybrid tracking system using BIM and multimodal sensors",
    journal = "Advanced Engineering Informatics",
    volume = "32",
    pages = "126 - 138",
    year = "2017",
    issn = "1474-0346",
    doi = "https://doi.org/10.1016/j.aei.2017.02.001",
    url = "http://www.sciencedirect.com/science/article/pii/S147403461630252X",
    author = "JeeWoong Park and Jingdao Chen and Yong K. Cho",
    }
  19. Chen, J., Fang, Y., Cho, Y., Kim, C. (2017).
    Principal Axes Descriptor (PAD) for Automated Construction Equipment Classification from Point Clouds.
    ASCE's Journal of Computing in Civil Engineering, Volume 31, Issue 2, March 2017, DOI: 10.1061/(ASCE)CP.1943-5487.0000628
    [bibtex]
    @article{chen2017pad,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho and Changwan Kim },
    title = {Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds},
    journal = {Journal of Computing in Civil Engineering},
    volume = {31},
    number = {2},
    pages = {04016058},
    year = {2017},
    doi = {10.1061/(ASCE)CP.1943-5487.0000628}
    }

    20. 2016

  20. Fang, Y.,Cho, Y., and Chen, J. (2016).
    A Framework for Real-time Pro-active Safety Assistance for Mobile Crane Lifting Operations.
    Automation in Construction, Volume 72, Part 3, December 2016, Pages 367-379, DOI: 10.1016/j.autocon.2016.08.025
    [bibtex]
    @article{fang2016autcon,
    title = "A framework for real-time pro-active safety assistance for mobile crane lifting operations",
    journal = "Automation in Construction",
    volume = "72",
    pages = "367 - 379",
    year = "2016",
    issn = "0926-5805",
    doi = "https://doi.org/10.1016/j.autcon.2016.08.025",
    url = "http://www.sciencedirect.com/science/article/pii/S0926580516301807",
    author = "Yihai Fang and Yong K. Cho and Jingdao Chen",
    }

    2024

  1. Yu, J., Saha, S., Jayakumar, M., Gugssa, M., Chen, J., and Wang, J. (2024).
    LiDAR-based Traversability Estimation for Ground Robots on Construction Sites using Self-Supervised Learning
    Proceedings of the ASCE 2024 International Conference on Computing in Civil Engineering (i3CE), Pittsburgh, PA, USA, July 28-31
    [bibtex]
  2. Rugg, J., Chen, J., Gugssa, M., and Wang, J. (2024).
    Object-level Temporal Change Detection on Construction Sites with 3D Deep Learning Models
    Proceedings of the ASCE 2024 International Conference on Computing in Civil Engineering (i3CE), Pittsburgh, PA, USA, July 28-31
    [bibtex]
  3. Chang, S., Chen, J., and Park, J. (2024).
    Review of Metaverse Technologies to Broaden Accessibility in Civil and Construction Engineering Education
    ASCE Construction Research Congress (CRC) 2024, March 20-23, Des Moines, IA.
    [bibtex]
    @inbook{chang2024,
    author = {Soowon Chang and Jingdao Chen and Jisoo Park },
    title = {Review of Metaverse Technologies to Broaden Accessibility in Civil and Construction Engineering Education},
    booktitle = {Construction Research Congress 2024},
    chapter = {},
    pages = {304-314},
    doi = {10.1061/9780784485293.031},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784485293.031},
    }

    4. 2023

  4. Gao, K., Haverly, A., Mittal, S., and Chen, J. (2023).
    A bibliometric view of AI Ethics development
    2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Yanuca Island, Fiji, Dec 4-6,
    [bibtex]
    @INPROCEEDINGS{gao2023,
    author={Gao, Kevin Di and Haverly, Andrew and Mittal, Sudip and Chen, Jingdao},
    booktitle={2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)},
    title={A Bibliometric View of AI Ethics Development},
    year={2023},
    volume={},
    number={},
    pages={1-5},
    doi={10.1109/CSDE59766.2023.10487710},
    }
  5. Garshabi, A., Wang, J., Chen, J., and Ma, J. (2023).
    Human-Robot Collaboration in the Construction Industry: A Mini-review
    2023 IEEE International Conference on Robotics and Automation (ICRA) Workshop on Future of Construction, London, U.K., June 2,
    [bibtex]
    @inproceedings{garshasbi2023,
    doi = {10.22260/ICRA2023/0003},
    year = 2023,
    month = {July},
    author = {Garshasbi, Ali and Wang, Jun and Chen, Jingdao and Ma, Junfeng},
    title = {Human-Robot Collaboration in the Construction Industry: A Mini-review},
    booktitle = {Proceedings of the 2nd Future of Construction Workshop at the International Conference on Robotics and Automation (ICRA 2023)},
    isbn = {-},
    issn = {2413-5844},
    publisher = {International Association for Automation and Robotics in Construction (IAARC)},
    editor = {Chen, Jingdao (Mississippi State University) and Cho, Yong K. (Georgia Institute of Technology) and Jeong, Inbae (North Dakota State University) and Feng, Chen (New York University) and Zhang, Liangjun (Baidu Research) and Fallon, Maurice (University of Oxford) and Morin, Kristian (HILTI) and Nair, Ashish-Devadas (HILTI)},
    pages = {1-4},
    address = {London, UK},
    }
  6. Chen, J., Gugssa, M., Yee, J., Wang, J., Goodin, C., and Ram Das, A. (2023).
    Framework for digital twin creation in off-road environments from LiDAR scans
    Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290F (13 June 2023)
    [code] [bibtex]
    @inproceedings{chen2023spie,
    author = {Jingdao Chen and Mikias Gugssa and Justin Yee and Jun Wang and Christopher Goodin and Athish Ram Das},
    title = {{Framework for digital twin creation in off-road environments from LiDAR scans}},
    volume = {12529},
    booktitle = {Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications},
    editor = {Christopher L. Howell and Kimberly E. Manser and Raghuveer M. Rao},
    organization = {International Society for Optics and Photonics},
    publisher = {SPIE},
    pages = {125290F},
    keywords = {LiDAR, Off-road, Digital twin, Point cloud, Simulator},
    year = {2023},
    doi = {10.1117/12.2663632},
    URL = {https://doi.org/10.1117/12.2663632},
    }
  7. Yu, J., Chen, J., Dabbiru, L., and Goodin, C. (2023).
    Analysis of LiDAR configurations on off-road semantic segmentation performance
    Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, 1254003 (13 June 2023)
    [code] [bibtex]
    @inproceedings{yu2023spie,
    author = {Jinhee Yu and Jingdao Chen and Lalitha Dabbiru and Christopher T. Goodin},
    title = {{Analysis of LiDAR configurations on off-road semantic segmentation performance}},
    volume = {12540},
    booktitle = {Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023},
    editor = {Michael C. Dudzik and Stephen M. Jameson and Theresa J. Axenson},
    organization = {International Society for Optics and Photonics},
    publisher = {SPIE},
    pages = {1254003},
    keywords = {LiDAR , autonomous vehicles, semantic segmentation},
    year = {2023},
    doi = {10.1117/12.2663098},
    URL = {https://doi.org/10.1117/12.2663098},
    }
  8. Goh, E., Ward, I. R., Vincent, G., Pak, K., Chen, J., and Wilson, B. (2023).
    Self-supervised Distillation for Computer Vision Onboard Planetary Robots
    IEEE Aerospace Conference, Big Sky, MT, USA, March 4-11
    [code] [bibtex]
    @INPROCEEDINGS{goh2023,
    author={Goh, Edwin and Ward, Isaac R. and Vincent, Grace and Pak, Kai and Chen, Jingdao and Wilson, Brian},
    booktitle={2023 IEEE Aerospace Conference},
    title={Self-supervised Distillation for Computer Vision Onboard Planetary Robots},
    year={2023},
    volume={},
    number={},
    pages={1-11},
    doi={10.1109/AERO55745.2023.10115598},
    }

    9. 2022

  9. Ward, I. R., Moore, C., Pak, K., Chen, J., and Goh, E. (2022).
    Improving Contrastive Learning on Visually Homogeneous Mars Rover Images
    European Conference on Computer Vision (ECCV) Workshop on AI4Space, Tel Aviv, Israel, Oct 23
    [bibtex]
    @InProceedings{ward2022,
    author="Ward, Isaac Ronald and Moore, Charles and Pak, Kai and Chen, Jingdao and Goh, Edwin",
    editor="Karlinsky, Leonid and Michaeli, Tomer and Nishino, Ko",
    title="Improving Contrastive Learning on Visually Homogeneous Mars Rover Images",
    booktitle="Computer Vision -- ECCV 2022 Workshops",
    year="2023",
    publisher="Springer Nature Switzerland",
    address="Cham",
    pages="170--185",
    isbn="978-3-031-25056-9",
    }
  10. Vincent, G., Yepremyan, A., Chen, J., and Goh, E. (2022).
    Mixed-Domain Training Improves Multi-Mission Terrain Segmentation
    European Conference on Computer Vision (ECCV) Workshop on AI4Space, Tel Aviv, Israel, Oct 23
    [bibtex]
    @InProceedings{vincent2022,
    author="Vincent, Grace and Yepremyan, Alice and Chen, Jingdao and Goh, Edwin",
    editor="Karlinsky, Leonid and Michaeli, Tomer and Nishino, Ko",
    title="Mixed-Domain Training Improves Multi-mission Terrain Segmentation",
    booktitle="Computer Vision -- ECCV 2022 Workshops",
    year="2023",
    publisher="Springer Nature Switzerland",
    address="Cham",
    pages="96--111",
    isbn="978-3-031-25056-9",
    }
  11. Kim, S., Yajima, Y., Park, J., Chen, J., and Cho,Y. (2022).
    A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds.
    Proceedings of the 9th International Conference on Construction Engineering and Project Management (ICCEPM), Las Vegas, NV, USA, June 20-23
    [code] [bibtex]
    @INPROCEEDINGS{kim2022iccepm,
    author={Kim, Seongyong and Yajima, Yosuke and Park, Jisoo and Chen, Jingdao and Cho, Yong},
    booktitle={2022 International Conference on Construction Engineering and Project Management (ICCEPM)},
    title={A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds},
    year={2022},
    pages={792-799},
    }
  12. Goh, E., Chen, J., and Wilson, B. (2022).
    Mars Terrain Segmentation with Less Labels.
    IEEE Aerospace Conference, Big Sky, MT, USA, March 5-12
    [bibtex]
    @INPROCEEDINGS{goh2022,
    author={Goh, Edwin and Chen, Jingdao and Wilson, Brian},
    booktitle={2022 IEEE Aerospace Conference (AERO)},
    title={Mars Terrain Segmentation with Less Labels},
    year={2022},
    pages={1-10},
    doi={10.1109/AERO53065.2022.9843245},
    }

    13. 2021

  13. Yajima, Y., Kim, S., Chen, J., and Cho,Y. (2021).
    Fast Online Incremental Segmentation of 3D Point Clouds from Disaster Sites.
    Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC), Dubai, UAE, November 2-5
    [bibtex]
    @inproceedings{yajima2021isarc,
    doi = {10.22260/ISARC2021/0048},
    year = 2021,
    month = {November},
    author = {Yajima, Yosuke and Kim, Seongyong and Chen, Jing Dao and Cho, Yong},
    title = {Fast Online Incremental Segmentation of 3D Point Clouds from Disaster Sites},
    booktitle = {Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)},
    isbn = {978-952-69524-1-3},
    issn = {2413-5844},
    publisher = {International Association for Automation and Robotics in Construction (IAARC)},
    pages = {341-348},
    address = {Dubai, UAE},
    }
  14. Kahoush, M., Yajima, Y., Kim, S., Chen, J., Park, J., Kangisser, S., Irizarry, J., and Cho,Y. (2021).
    Analysis of Flight Parameters on UAV Semantic Segmentation Performance for Highway Infrastructure Monitoring.
    Proceedings of the ASCE 2021 International Conference on Computing in Civil Engineering (i3CE), Orlando, FL, USA, September 12-14
    [bibtex]
    @inproceedings{kahoush2021i3ce,
    author = {Mark Kahoush and Yosuke Yajima and Seongyong Kim and Jingdao Chen and Jisoo Park and Steven Kangisser and Javier Irizarry and Yong K. Cho },
    title = {Analysis of Flight Parameters on UAV Semantic Segmentation Performance for Highway Infrastructure Monitoring},
    booktitle = {International Conference on Computing in Civil Engineering 2021},
    year = 2021,
    pages = {885-893},
    doi = {10.1061/9780784483893.109},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784483893.109},
    }
  15. Yajima, Y., Kahoush, M., Kim, S., Chen, J., Park, J., Kangisser, S., Irizarry, J., and Cho,Y. (2021).
    AI-driven 3D Point Cloud-Based Highway Infrastructure Monitoring System using UAV.
    Proceedings of the ASCE 2021 International Conference on Computing in Civil Engineering (i3CE), Orlando, FL, USA, September 12-14
    [bibtex]
    @inproceedings{yajima2021i3ce,
    author = {Yosuke Yajima and Mark Kahoush and Seongyong Kim and Jingdao Chen and Jisoo Park and Steven Kangisser and Javier Irizarry and Yong K. Cho },
    title = {AI-Driven 3D Point Cloud-Based Highway Infrastructure Monitoring System Using UAV},
    booktitle = {International Conference on Computing in Civil Engineering 2021},
    year = 2021,
    pages = {894-901},
    doi = {10.1061/9780784483893.110},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784483893.110},
    }

    16. 2020

  16. Chen, J., Kim, P., Sun, D.I., Han, C.S., Ahn, Y.H., Ueda, J. and Cho, Y. (2020).
    Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds.
    Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), Kitakyshu, Japan, October 27-28
    [bibtex]
    @INPROCEEDINGS{chen2020isarc,
    author={Jingdao Chen and Pileun Kim and Dong-Ik Sun and Chang-Soo Han and Yong-Han Ahn and Jun Ueda and Yong K. Cho},
    booktitle={37th International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds},
    year={2020},
    month={October},
    pages={781-788},
    }
  17. Price, L., Chen, J., and Cho, Y. (2020).
    Dynamic Crane Workspace Update for Collision Avoidance during Blind Lift Operations.
    Proceedings of the 18th International Conference on Computing in Civil and Building Engineering, ICCCBE, pp. 959-970, São Paulo, Brazil
    [bibtex]
    @InProceedings{price2020,
    author="Price, Leon C. and Chen, Jingdao and Cho, Yong K.",
    editor="Toledo Santos, Eduardo and Scheer, Sergio",
    title="Dynamic Crane Workspace Update for Collision Avoidance During Blind Lift Operations",
    booktitle="Proceedings of the 18th International Conference on Computing in Civil and Building Engineering",
    year="2020",
    publisher="Springer International Publishing",
    address="Cham",
    pages="959--970",
    isbn="978-3-030-51295-8",
    }
  18. Chen, J., and Cho, Y. (2020).
    Unsupervised Crack Segmentation from Disaster Site Point Clouds using Point Feature Clustering.
    Proceedings of Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE, pp. 125-133, Berlin, Germany
    [bibtex]
    @inproceedings {chen2020egice,
    author = {Chen, Jingdao and Cho, Yong},
    editor = {Ungureanu, Lucian Constantin AND Hartmann, Timo},
    title = {Unsupervised Crack Segmentation from Disaster Site Point Clouds using Point Feature Clustering},
    booktitle = {EG-ICE 2020 Workshop on Intelligent Computing in Engineering},
    year = {2020},
    publisher = {Universitätsverlag der TU Berlin},
    address = {Berlin},
    doi = {10.14279/depositonce-9977},
    url = {http://dx.doi.org/10.14279/depositonce-9977},
    pages = {125-133},
    }
  19. Park, J., Chen, J., and Cho Y. (2020).
    Point Cloud Information Modeling (PCIM): an Innovative Framework for as-is Information Modeling of Construction Sites
    ASCE Construction Research Congress (CRC) 2020, March 9-10, Tempe, AZ.
    [bibtex]
    @inproceedings{park2020crc,
    author = {Jisoo Park and Jingdao Chen and Yong K. Cho },
    title = {Point Cloud Information Modeling (PCIM): an Innovative Framework for as-is Information Modeling of Construction Sites},
    booktitle = {Construction Research Congress 2020},
    year = {2020},
    }

    20. 2019

  20. Chen, J., and Cho Y. (2019).
    Exemplar-based Building Element Retrieval from Point Clouds
    International Conference on Smart Infrastructure and Construction (ICSIC), Churchill College, Cambridge, UK, July 8-9.
    [bibtex]
    @inproceedings{chen2019icsic,
    author = {Jingdao Chen and Yong Cho},
    title = {Exemplar-Based Building Element Retrieval from Point Clouds},
    booktitle = {International Conference on Smart Infrastructure and Construction 2019 (ICSIC)},
    year = "2019",
    pages = {225-231},
    doi = {10.1680/icsic.64669.225},
    URL = {https://www.icevirtuallibrary.com/doi/abs/10.1680/icsic.64669.225},
    }
  21. Chen, J. and Cho, Y. (2019).
    Detection of Damaged Infrastructure on Disaster Sites using Mobile Robots.
    IEEE 2019 16th International Conference on Ubiquitous Robots (UR), Jeju, Korea, June 24-27
    [bibtex]
    @INPROCEEDINGS{chen2019ur,
    author={J. {Chen} and Y. K. {Cho}},
    booktitle={2019 16th International Conference on Ubiquitous Robots (UR)},
    title={Detection of Damaged Infrastructure on Disaster Sites using Mobile Robots},
    year={2019},
    pages={648-653},
    }
  22. Chen, J., Kim, K.N., Cho,Y., Lee, J., Kim, B., Ahn, Y., and Kang, J. (2019).
    Nuclear Power Plant Disaster Site Simulation using Rigid Body Physics.
    Proceedings of the ASCE 2019 International Conference on Computing in Civil Engineering (i3CE), Atlanta, GA, USA, June 17-19, DOI:10.1061/9780784482421.069
    [bibtex]
    @inproceedings{chen2019i3ce,
    author = {Jingdao Chen and Kinam Kim and Yong K. Cho and Joo Sung Lee and Byeol Kim and Yong Han Ahn and Junsuk Kang },
    title = {Nuclear Power Plant Disaster Site Simulation Using Rigid Body Physics},
    booktitle = {International Conference on Computing in Civil Engineering 2019},
    year={2019},
    pages = {546-552},
    doi = {10.1061/9780784482421.069},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784482421.069},
    }
  23. Kim, K.N., Chen, J., and Cho, Y. (2019).
    Evaluation of Machine Learning Algorithms for Worker's Motion Recognition using Motion Sensors.
    Proceedings of the ASCE 2019 International Conference on Computing in Civil Engineering (i3CE), Atlanta, GA, USA, June 17-19, DOI:10.1061/9780784482438.007
    [bibtex]
    @inproceedings{kim2019i3ce,
    author = {Kinam Kim and Jingdao Chen and Yong K. Cho },
    title = {Evaluation of Machine Learning Algorithms for Worker's Motion Recognition Using Motion Sensors},
    booktitle = {International Conference on Computing in Civil Engineering 2019},
    year={2019},
    pages = {51-58},
    doi = {10.1061/9780784482438.007},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784482438.007},
    }

    24. 2018

  24. Chen, J., Kim, P., Cho, Y., and Ueda, J. (2018).
    Object-sensitive potential fields for mobile robot navigation and mapping in indoor environments.
    Proceedings of the 2018 IEEE 15th International Conference on Ubiquitous Robots (UR), Honolulu, HI, USA, June 26-30, 10.1109/URAI.2018.8441896
    [bibtex]
    @INPROCEEDINGS{chen2018ur,
    author={J. {Chen} and P. {Kim} and Y. K. {Cho} and J. {Ueda}},
    booktitle={2018 15th International Conference on Ubiquitous Robots (UR)},
    title={Object-sensitive potential fields for mobile robot navigation and mapping in indoor environments},
    year={2018},
    pages={328-333},
    doi={10.1109/URAI.2018.8441896},
    month={June},
    }
  25. Chen, J., Cho, Y., and Ueda, J. (2018).
    Sampled-Point Network for Classification of Deformed Building Element Point Clouds.
    Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), BrisBane, Australia, May 21-25, 10.1109/ICRA.2018.8461095
    [code] [bibtex]
    @INPROCEEDINGS{chen2018icra,
    author={J. {Chen} and Y. K. {Cho} and J. {Ueda}},
    booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
    title={Sampled-Point Network for Classification of Deformed Building Element Point Clouds},
    year={2018},
    pages={2164-2169},
    }
  26. Fang, Y., Chen, J., Cho, Y., Zhang, S., and Perez, E. (2018).
    Enhance Blind Lift Safety by Real-Time Sensing and Visualization.
    Proceedings of the 18th International Conference on Construction Applications of Virtual Reality (CONVR2018), Auckland, New Zealand, Nov 22-23
    [bibtex]
    @INPROCEEDINGS{fang2018convr,
    author={Yihai Fang and Jingdao Chen and Yong Cho and Sijie Zhang and Esau Perez},
    booktitle={18th International Conference on Construction Applications of Virtual Reality (CONVR)},
    title={Enhance Blind Lift Safety by Real-Time Sensing and Visualization},
    year={2018},
    month={November},
    }
  27. Kim, P., Chen, J., Kim, J., and Cho, Y. (2018).
    SLAM-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications.
    Proceedings of Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE,. pp. 254-269, Lausanne, Switzerland, DOI: 10.1007/978-3-319-91635-4_14
    [bibtex]
    @InProceedings{kim2018egice,
    author="Kim, Pileun and Chen, Jingdao and Kim, Jitae and Cho, Yong K.",
    editor="Smith, Ian F. C. and Domer, Bernd",
    title="SLAM-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications",
    booktitle="Advanced Computing Strategies for Engineering",
    year="2018",
    publisher="Springer International Publishing",
    pages="254--269",
    }
  28. Chen, J. and Cho, Y. (2018).
    Point-to-point Comparison Method for Automated Scan-vs-BIM Deviation Detection.
    Proceedings of 17th International Conference on Computing in Civil and Building Engineering, Tampere, Finland, June 4-7.
    [bibtex]
    @INPROCEEDINGS{chen2018icccbe,
    author={Jingdao Chen and Yong Cho},
    booktitle={17th International Conference on Computing in Civil and Building Engineering},
    title={Point-to-point Comparison Method for Automated Scan-vs-BIM Deviation Detection},
    year={2018},
    month={June},
    }
  29. Kim, P., Chen, J., Cho, Y. (2018).
    Autonomous Mobile Robot Localization and Mapping for Unknown Construction Environments.
    ASCE Construction Research Congress (CRC) 2018, pp.147-156, April 2-4, New Orleans, LA, DOI: 10.1061/9780784481264.015
    [bibtex]
    @inproceedings{kim2018crc,
    author = {Pileun Kim and Jingdao Chen and Yong K. Cho },
    title = {Autonomous Mobile Robot Localization and Mapping for Unknown Construction Environments},
    booktitle = {Construction Research Congress 2018},
    year = {2018},
    pages = {147-156},
    doi = {10.1061/9780784481264.015},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784481264.015},
    }
  30. Chen, J., Cho, Y., and Kim, K. (2018).
    Region Proposal Mechanism for Building Element Recognition for Advanced Scan-to-BIM Process.
    ASCE Construction Research Congress 2018,April2-4, New Orleans, LA, Doi: 10.1061/9780784481264.022
    [bibtex]
    @inproceedings{chen2018crc,
    author = {Jingdao Chen and Yong K. Cho and Kyungki Kim },
    title = {Region Proposal Mechanism for Building Element Recognition for Advanced Scan-to-BIM Process},
    booktitle = {Construction Research Congress 2018},
    year = {2018},
    pages = {221-231},
    doi = {10.1061/9780784481264.022},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784481264.022},
    }

    31. 2017

  31. Kim, P., Chen, J., and Cho, Y. (2017).
    Building element recognition with thermal-mapped point clouds.
    Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC), Taipei, Taiwan, June 28-July 1, DOI: 10.22260/ISARC2017/0122
    [bibtex]
    @INPROCEEDINGS{kim2017isarc,
    author={Pileun Kim and Jingdao Chen and Yong Cho},
    booktitle={34th International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Building element recognition with thermal-mapped point clouds},
    year={2017},
    month={June},
    }
  32. Chen, J., Fang, Y., and Cho, Y. (2017).
    Mobile Asset Tracking for Dynamic 3D Crane Workspace Generation in Real Time.
    Proceedings of the 2017 International Workshop on Computing for Civil Engineering (IWCCE), Seattle, WA, USA, June 25-27, DOI: 10.1061/9780784480830.016
    [bibtex]
    @inproceedings{chen2017crane,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho },
    title = {Mobile Asset Tracking for Dynamic 3D Crane Workspace Generation in Real Time},
    booktitle = {International Workshop on Computing in Civil Engineering 2017},
    year = {2017},
    pages = {122-129},
    doi = {10.1061/9780784480830.016},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784480830.016},
    }
  33. Chen, J., Fang, Y., and Cho, Y. (2017).
    Unsupervised Recognition of Volumetric Structural Components from Building Point Clouds.
    Proceedings of the 2017 International Workshop on Computing for Civil Engineering (IWCCE), Seattle, WA, USA, June 25-27, DOI: 10.1061/9780784480823.005
    [bibtex]
    @inproceedings{chen2017iwcce,
    author = {Jingdao Chen and Yihai Fang and Yong K. Cho },
    title = {Unsupervised Recognition of Volumetric Structural Components from Building Point Clouds},
    booktitle = {International Workshop on Computing in Civil Engineering 2017},
    year = {2017},
    pages = {34-42},
    doi = {10.1061/9780784480823.005},
    URL = {https://ascelibrary.org/doi/abs/10.1061/9780784480823.005},
    }

    34. 2016

  34. Kim, P., Cho, Y., and Chen, J. (2016).
    Automatic Registration of Laser Scanned Color Point Clouds Based on Common Feature Extraction.
    16th International Conference on Construction Applications of Virtual Reality (CONVR), Hong Kong, Dec. 11-13
    [bibtex]
    @INPROCEEDINGS{chen2016convr,
    author={Pileun Kim and Yong Cho and Jingdao Chen},
    booktitle={16th International Conference on Construction Applications of Virtual Reality (CONVR)},
    title={Automatic Registration of Laser Scanned Color Point Clouds Based on Common Feature Extraction},
    year={2016},
    month={December},
    }
  35. Chen, J., Fang, Y., and Cho, Y. (2016).
    Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management.
    International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0027
    [bibtex]
    @INPROCEEDINGS{chen2016isarc,
    author={Jingdao Chen and Yihai Fang and Yong Cho},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management},
    year={2016},
    month={July},
    }
  36. Chen, J. and Cho, Y. (2016).
    Real-time 3D Mobile Mapping for the Built Environment
    . International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0028
    [bibtex]
    @INPROCEEDINGS{chen2016slam,
    author={Jingdao Chen and Yong Cho},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Real-time 3D Mobile Mapping for the Built Environment},
    year={2016},
    month={July},
    }
  37. Fang, Y., Chen, J., Cho, Y., and Zhang, P. (2016).
    A Point Cloud-Vision Hybrid Approach for 3D Location Tracking of Mobile Construction Assets.
    International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0074
    [bibtex]
    @INPROCEEDINGS{fang2016isarc,
    author={Yihai Fang and Jingdao Chen and Yong Cho and Peiyao Zhang},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={A Point Cloud-Vision Hybrid Approach for 3D Location Tracking of Mobile Construction Assets},
    year={2016},
    month={July},
    }
  38. Kim, P., Cho, Y. Chen, J. (2016).
    Target-Free Automatic Registration of Point Clouds.
    International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, July 18-21, 2016, DOI: 10.22260/ISARC2016/0083
    [bibtex]
    @INPROCEEDINGS{kim2016isarc,
    author={Pileun Kim and Yong Cho and Jingdao},
    booktitle={33rd International Symposium on Automation and Robotics in Construction (ISARC)},
    title={Target-Free Automatic Registration of Point Clouds},
    year={2016},
    month={July},
    }

    2024

  1. Chen, J., Cho, Y., Feng, C., Jeong, I., Zhang, L., Fallon, M., Helmberger, M., Morin, K., and Garcia de Soto, B.(2024).
    Future of Construction: Lifelong Learning Robots in Changing Construction Sites
    Organizing committee. Workshop at the IEEE Conference on Robotics and Automation (ICRA), May 13, 2024.
  2. Chen, J. (2024).
    Deep Learning Tools for Understanding and Modeling the Built Environment
    Presenter. Workshop at the ASCE International Conference on Computing in Civil Engineering (I3CE), July 28, 2024

    3. 2023

  3. Chen, J., Cho, Y., Feng, C., Jeong, I., Zhang, L., Fallon, M., Helmberger, M., Morin, K., and Nair A.(2023).
    Future of Construction: Robot Perception, Mapping, Navigation, Control in Unstructured and Cluttered Environments
    Organizing committee. Workshop at the IEEE Conference on Robotics and Automation (ICRA), June 2, 2023.

    4. 2022

  4. Chen, J. (2022).
    Tutorial on Scan-to-BIM using Python and Open3D
    Presenter. Workshop at the International Conference on Construction Engineering and Project Management (ICCEPM), June 24, 2022.
  5. Chen, J., Cho, Y., Feng, C., Jeong, I., and Zhang, L. (2022).
    Future of Construction: Build Faster, Better, Safer - Together with Robots
    Organizing committee. Workshop at the IEEE Conference on Robotics and Automation (ICRA), May 23, 2022.

    6. 2021

  6. Chen, J., Park, J., Yajima, Y., Kim, S. (2021).
    GTS2B
    1st Workshop and Challenge on Computer Vision in the Built Environment. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 20, 2021.

    2022

  1. Chen, J., and Cho, Y. (2022).
    Rapid scan-to-building information modeling using robotics and artificial intelligence for construction applications
    Research Companion to Building Information Modeling, March 2022
    [bibtex]
    @incollection{chen2022bim,
    title={Rapid scan-to-building information modeling using robotics and artificial intelligence for construction applications},
    author={Chen, J., and Cho, Y.},
    editor={Lu, W. and Anumba, C.J.},
    booktitle={Research Companion to Building Information Modeling},
    isbn={9781839105517},
    series={Elgar Companions to the Built Environment Series},
    url={https://books.google.com/books?id=stqZzgEACAAJ},
    year={2022},
    publisher={Edward Elgar Publishing, Incorporated},
    }

Teaching

Courses at Mississippi State University:
  • Fall 2024 - CSE 4643/6643: AI Robotics
  • Spring 2024 - CSE 4633/6633: Artificial Intelligence
  • Fall 2023 - CSE 4643/6643: AI Robotics
  • Spring 2023 - CSE 4633/6633: Artificial Intelligence
  • Fall 2022 - CSE 4643/6643: AI Robotics
  • Spring 2022 - CSE 8990: Special Topics in CS: Advanced AI Robotics
  • Fall 2021 - CSE 4643/6643: AI Robotics

Join

Students with experience in the areas of robotics, machine learning, artificial intelligence, or computer vision are encouraged to apply. Mississippi State University has fully-online PhD in Computer Science programs available. Please send your latest CV to chenjingdao@cse.msstate.edu if you are interested in joining our research group.

Code

Learnable Region Growing

Code to perform class-agnostic 3D point cloud segmentation using a learnable region growing method. Implemented in Tensorflow.

Infrared Segmentation

Code to perform person detection by semantic segmentation from night-time infrared (IR) images. Implemented in Tensorflow.

Multi-view Incremental Segmentation

Code to incrementally perform semantic instance segmentation of laser-scanned 3D point clouds. Implemented in ROS + Tensorflow.

Point Cloud Scene Completion

Code to perform scene completion of obstructed building facades using generative adversarial inpainting. Implemented in Tensorflow.

Off-Road Digital Twin

Code to create digital twins of off-road environments, including tree segmentation and terrain modeling.

LiDAR Configuration Analysis

Code to analyze effect of LiDAR configuration change on semantic segmentation performance. Evaluated on MAVS and RELLIS-3D datasets.

Scan-to-BIM

Code to create IFC building models from point cloud data, using Pytorch and Open3D (presented at ICCEPM 2022). Jupyter notebooks and slides available here.

URA*: Uncertainty-aware A*

Code for uncertainty-aware path planning in off-road environments using Python (submitted to ICRA 2024). Jupyter notebooks and datasets available.

LiDAR Processing Practice

Jupyter notebook for practicing LiDAR point cloud processing algorithms such as visualization, ground filtering, filtering by distance, and Euclidean clustering.

Python Coding Exercises

A series of intro-level Python coding exercises for scientific computing applications. Jupyter notebook available here.

Gallery