Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments
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Frederik E. T. Schöller | A. Agha | K. Otsu | Xianmei Lei | Edward Terry | Fernando Chavez | Thomas Touma | Taeyeon Kim | Daniel Pastor | Barry Ridge | Nicolas Marchal
[1] Jay L. Gao,et al. NeBula: TEAM CoSTAR's Robotic Autonomy Solution that Won Phase II of DARPA Subterranean Challenge , 2022, Field Robotics.
[2] Roland Siegwart,et al. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge , 2022, Field Robotics.
[3] Eric W. Frew,et al. Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration , 2021, Field Robotics.
[4] Katrina Lo Surdo,et al. Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61's Approach to the DARPA Subterranean Challenge , 2021, Field Robotics.
[5] Geoffrey A. Hollinger,et al. Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots , 2022 .
[6] S. Bhattacharyya,et al. Plug-and-Play Deblurring for Robust Object Detection , 2021, 2021 International Conference on Visual Communications and Image Processing (VCIP).
[7] Geoffrey A. Hollinger,et al. Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection , 2021, IEEE Robotics and Automation Letters.
[8] Jay L. Gao,et al. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge , 2021, ArXiv.
[9] Amanda Bouman,et al. Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[10] Eric Heiden,et al. LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[11] Christos Papachristos,et al. Multi-modal Visual-Thermal Saliency-based Object Detection in Visually-degraded Environments , 2020, 2020 IEEE Aerospace Conference.
[12] Abel Gawel,et al. Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes , 2019, IEEE Robotics and Automation Letters.
[13] Quoc V. Le,et al. Learning Data Augmentation Strategies for Object Detection , 2019, ECCV.
[14] Shreyansh Daftry,et al. Object and Gas Source Detection with Robotic Platforms in Perceptually-Degraded Environments , 2020 .
[15] Martin Saska,et al. DARPA Subterranean Challenge: Multi-robotic Exploration of Underground Environments , 2019, MESAS.
[16] Jeremy Kepner,et al. Survey and Benchmarking of Machine Learning Accelerators , 2019, 2019 IEEE High Performance Extreme Computing Conference (HPEC).
[17] Arash Vahdat,et al. A Robust Learning Approach to Domain Adaptive Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Gabriel J. Brostow,et al. Digging Into Self-Supervised Monocular Depth Estimation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[20] Lars Petersson,et al. DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[21] G. Raj,et al. Blur image detection using Laplacian operator and Open-CV , 2016, 2016 International Conference System Modeling & Advancement in Research Trends (SMART).
[22] F. Dellaert. Factor Graphs and GTSAM: A Hands-on Introduction , 2012 .
[23] William Whittaker,et al. Robotic introspection for exploration and mapping of subterranean environments , 2007 .
[24] Fredric C. Gey,et al. The Relationship between Recall and Precision , 1994, J. Am. Soc. Inf. Sci..