pith. sign in

arxiv: 2407.00848 · v6 · submitted 2024-06-30 · 💻 cs.RO

EgoExo++: Integrating On-demand Exocentric Visuals with 2.5D Ground Surface Estimation for Interactive Teleoperation of Underwater ROVs

Adnan Abdullah , Ruo Chen , Ioannis Rekleitis , Md Jahidul Islam This is my paper

Pith reviewed 2026-05-23 23:04 UTC · model grok-4.3

classification 💻 cs.RO
keywords underwater ROVteleoperationexocentric visualization2.5D terrain estimationmonocular SLAMuser studyshared autonomyvisual SLAM
0
0 comments X

The pith

EgoExo++ generates on-demand exocentric views plus 2.5D ground estimates from a single egocentric camera to improve underwater ROV teleoperation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that closed-form synthesis of third-person visuals and piecewise planar ground surfaces can be added to any monocular-SLAM pipeline without extra sensors or anchors. This gives operators ground-relative cues such as clearance checks and terrain marker following that pure first-person video cannot supply. Sympathetic readers would care because current egocentric feeds restrict field of view and raise collision risk in turbid, low-light water; the reported user studies record 16 percent faster missions, fivefold lower path deviation, and fewer collisions when the augmented views are available.

Core claim

EgoExo++ augments 2D exocentric view synthesis with on-the-fly piecewise planar 2.5D ground surface estimation. Both steps are closed-form, rely only on egocentric images and monocular SLAM poses, and produce an anchor-free aerial viewpoint that directly supports ground-relative reasoning such as clearance estimation and terrain-based navigation marker following. Geometric accuracy is verified in 2-DOF indoor and 6-DOF underwater cave trials; two 15-participant user studies then show improved SUS scores, lower NASA-TLX workload, 16 percent faster missions, fivefold reduction in path deviation ratio, and fewer collisions (2 versus 5) relative to baseline egocentric teleoperation.

What carries the argument

The anchor-free aerial viewpoint with piecewise planar 2.5D surface fitting, which performs closed-form view synthesis and plane estimation from monocular SLAM poses to enable ground-relative reasoning.

If this is right

  • Ground-relative tasks such as clearance verification and terrain marker following become possible without external cameras or fiducials.
  • The method ports directly to existing teleoperation engines because it uses only egocentric images and standard monocular SLAM outputs.
  • Objective performance gains appear in both simulation and real cave data: 16 percent shorter missions, fivefold lower path deviation, and reduced collisions.
  • Augmented visuals support shared autonomy and embodied teleoperation by supplying operators with an additional spatial reference frame.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same closed-form pipeline could be tested on surface vehicles or aerial platforms operating in fog or dust where monocular SLAM is already available.
  • Integration with low-level obstacle avoidance controllers might further reduce the observed collision count by acting on the newly estimated ground plane.
  • Repeating the user studies with operators of varying experience levels would show whether the workload reduction holds for novices versus experts.
  • Extending the planar fit to a small number of non-ground surfaces could enable wall-relative navigation in confined caves without changing the core computation.

Load-bearing premise

Monocular SLAM estimates remain accurate and drift-free in low-light turbid water so that the synthesized views and plane fits stay geometrically correct.

What would settle it

A controlled underwater trial in which operators using the synthesized views record more collisions or larger path deviations than the same operators using only the raw egocentric feed would show the added visuals introduce net error.

Figures

Figures reproduced from arXiv: 2407.00848 by Adnan Abdullah, Ioannis Rekleitis, Md Jahidul Islam, Ruo Chen.

Figure 1
Figure 1. Figure 1: The proposed Ego-to-Exo teleoperation interface is demon [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed Ego-to-Exo problem formulation is shown; the idea is to generate EOB (Eye On the Back) views with augmented [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: We conduct 2D indoor navigation experiments with a [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The reconstructed 3D map of a cave segment in Devil’s Springs, Florida is shown. The colored points represent the tracked ORB [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A snapshot from our cave exploration scenario is shown: [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A demonstration of our adjustable EOB viewpoint feature is shown. The f numbers indicate the EOB distance from current to the reference frame used for the Ego-to-Exo view generation. Teleoperators can slide across the distance (f) and find the best exocentric view, which is f = 200 in this example [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Three challenging scenarios are shown for ROV teleopera [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

Underwater ROVs (Remotely Operated Vehicles) are indispensable for subsea exploration and task execution, yet typical teleoperation engines based on egocentric (first-person) video feeds restrict human operators' field-of-view and limit precise maneuvering in complex, unstructured underwater environments. To address this, we first propose EgoExo, a geometry-driven solution integrated into a visual SLAM pipeline that synthesizes on-demand exocentric (third-person) views from egocentric camera feeds. We further propose EgoExo++, which extends beyond 2D exocentric view synthesis (EgoExo) to augment a piecewise planar 2.5D ground surface estimation on-the-fly. Its anchor-free aerial viewpoint supports ground-relative reasoning, such as clearance and terrain-based navigation marker following. The computations involved are closed-form and rely solely on egocentric views and monocular SLAM estimates, which makes it portable across existing teleoperation engines and robust to varying waterbody characteristics. We validate the geometric accuracy of our approach through extensive experiments of 2-DOF indoor navigation and 6-DOF underwater cave exploration in challenging low-light conditions. To assess operational benefits, we conduct two user studies with simulation and real-world data, each involving 15 participants, comparing baseline egocentric teleoperation and EgoExo++. Results indicate improved system usability (SUS), reduced perceived workload (NASA-TLX), and significant gains in objective teleoperation performance, including 16% faster missions, 5-fold reduction in path deviation ratio, and fewer collision events (2 vs. 5 across trials). Furthermore, we highlight the role of EgoExo++ augmented visuals in supporting shared autonomy and embodied teleoperation. This new interactive approach to ROV teleoperation presents promising opportunities for future research in subsea telerobotics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes EgoExo++, which extends prior EgoExo work by integrating on-demand exocentric visual synthesis with piecewise planar 2.5D ground surface estimation for underwater ROV teleoperation. The method is presented as geometry-driven and closed-form, relying solely on egocentric camera feeds and monocular SLAM estimates to enable ground-relative reasoning such as clearance estimation and terrain marker following. Validation consists of 2-DOF indoor navigation and 6-DOF underwater cave experiments in low-light conditions, plus two 15-participant user studies reporting 16% faster missions, 5-fold reduction in path deviation ratio, and fewer collisions (2 vs. 5).

Significance. If the geometric accuracy of the synthesized exocentric views and 2.5D surfaces holds, the work provides a portable augmentation to existing teleoperation pipelines that could measurably improve operator situational awareness, reduce workload, and support shared autonomy in unstructured subsea settings.

major comments (1)
  1. [Validation experiments (as described in abstract and method)] The central claim that closed-form view synthesis and planar surface fitting produce geometrically correct exocentric visuals rests on the accuracy of monocular SLAM estimates in low-light, turbid conditions. The abstract states that geometric accuracy was validated in cave trials, yet no per-sequence SLAM metrics (ATE, RPE, scale drift) or error propagation analysis to plane parameters or viewpoint synthesis appear in the reported experiments. This omission is load-bearing because degraded feature tracking or inconsistent scale would render the anchor-free aerial viewpoint and clearance/terrain reasoning unreliable.
minor comments (1)
  1. [Abstract] The abstract reports concrete performance metrics from the user studies but does not indicate whether statistical tests (e.g., paired t-tests or Wilcoxon) were applied or how trial conditions were selected to avoid post-hoc bias.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing the need for explicit validation of the underlying monocular SLAM estimates. We address the major comment below and commit to strengthening the manuscript with additional analysis.

read point-by-point responses

  1. Referee: [Validation experiments (as described in abstract and method)] The central claim that closed-form view synthesis and planar surface fitting produce geometrically correct exocentric visuals rests on the accuracy of monocular SLAM estimates in low-light, turbid conditions. The abstract states that geometric accuracy was validated in cave trials, yet no per-sequence SLAM metrics (ATE, RPE, scale drift) or error propagation analysis to plane parameters or viewpoint synthesis appear in the reported experiments. This omission is load-bearing because degraded feature tracking or inconsistent scale would render the anchor-free aerial viewpoint and clearance/terrain reasoning unreliable.

    Authors: We acknowledge that the current manuscript does not report per-sequence SLAM metrics such as ATE, RPE, or scale drift, nor an explicit error propagation analysis. The geometric accuracy claim in the cave trials is supported by end-to-end task success and user-study performance gains, but we agree this is insufficient to fully substantiate the closed-form synthesis claims. In the revised version we will add: (i) ATE/RPE results for the indoor 2-DOF sequences against motion-capture ground truth; (ii) scale-drift and loop-closure consistency metrics for the underwater sequences; and (iii) a short error-propagation analysis relating typical SLAM covariance to synthesized viewpoint and plane-parameter uncertainty. These additions will appear in the experiments section. revision: yes

Circularity Check

0 steps flagged

No circularity: closed-form geometry from external SLAM estimates

full rationale

The paper presents EgoExo++ as a geometry-driven, closed-form pipeline that synthesizes exocentric views and fits piecewise planar 2.5D surfaces directly from monocular SLAM poses and points. No equations, parameters, or performance metrics are defined in terms of the target outputs; the method is explicitly stated to rely solely on egocentric inputs and SLAM estimates without fitting or self-referential definitions. No self-citation chains or uniqueness theorems are invoked to justify core steps. The reported gains (mission time, path deviation) are measured outcomes, not quantities forced by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text.

pith-pipeline@v0.9.0 · 5887 in / 1305 out tokens · 23035 ms · 2026-05-23T23:04:21.849448+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

55 extracted references · 55 canonical work pages

  1. [1]

    The Application of Fully Unmanned Robotic Systems for Inspection of Subsea Pipelines,

    A. G. Rumson, “The Application of Fully Unmanned Robotic Systems for Inspection of Subsea Pipelines,” Ocean Engineering , vol. 235, p. 109214, 2021

    work page 2021

  2. [2]

    New Frontiers in Ocean exploration: the Ocean Explo- ration Trust,

    S. Wishnak, “New Frontiers in Ocean exploration: the Ocean Explo- ration Trust,” NOAA Ocean Exploration, and Schmidt Ocean Institute 2021 Field Season , 2022

    work page 2021

  3. [3]

    Robotic survey and 3-d map- ping of underwater caves using a sunfish® autonomous underwater vehicle,

    V . Siegel, W. Stone, and K. Richmond, “Robotic survey and 3-d map- ping of underwater caves using a sunfish® autonomous underwater vehicle,” LPI Contributions, vol. 2697, p. 1037, 2023

    work page 2023

  4. [4]

    Underwater exploration and mapping,

    B. Joshi, M. Xanthidis, M. Roznere, N. J. Burgdorfer, P. Mordohai, A. Q. Li, and I. Rekleitis, “Underwater exploration and mapping,” in IEEE OES AUV Symposium , (Singapore), pp. 1–7, Sept. 2022

    work page 2022

  5. [5]

    American Cave Diving Fatalities 1969-2007,

    P. L. Buzzacott, E. Zeigler, P. Denoble, and R. Vann, “American Cave Diving Fatalities 1969-2007,” International Journal of Aquatic Research and Education , vol. 3, no. 2, p. 7, 2009

    work page 1969

  6. [6]

    Development of Intellectual Support System for ROV Operators,

    A. Y . Konoplin, N. Y . Konoplin, and V . Filaretov, “Development of Intellectual Support System for ROV Operators,” in IOP Conference Series: Earth and Environmental Science , vol. 272, p. 032101, IOP Publishing, 2019

    work page 2019

  7. [7]

    The Unknown and the Unexplored: Insights into the Pacific Deep-sea Following NOAA CAPSTONE Expeditions,

    B. R. Kennedy, K. Cantwell, M. Malik, C. Kelley, J. Potter, K. Elliott, E. Lobecker, L. M. Gray, D. Sowers, M. P. White, et al. , “The Unknown and the Unexplored: Insights into the Pacific Deep-sea Following NOAA CAPSTONE Expeditions,” Frontiers in Marine Science, vol. 6, p. 480, 2019

    work page 2019

  8. [8]

    Hovering Control of UUV through Underwater Object Detection Based on Deep Learning,

    H.-S. Jin, H. Cho, H. Jiafeng, J.-H. Lee, M.-J. Kim, S.-K. Jeong, D.-H. Ji, K. Joo, D. Jung, and H.-S. Choi, “Hovering Control of UUV through Underwater Object Detection Based on Deep Learning,” Ocean Engineering, vol. 253, p. 111321, 2022

    work page 2022

  9. [9]

    Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves,

    B. Yu, R. Tibbetts, T. Barna, A. Morales, I. Rekleitis, and M. J. Islam, “Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 9933–9940, IEEE, 2023

    work page 2023

  10. [10]

    Caveline detection at the edge for autonomous underwater cave exploration and mapping,

    M. Mohammadi, S.-E. Huang, T. Barua, I. Rekleitis, M. J. Islam, and R. Zand, “Caveline detection at the edge for autonomous underwater cave exploration and mapping,” in IEEE International Conference on Machine Learning and Applications (ICMLA) , (Jacksonville, FL, USA), Dec. 2023

    work page 2023

  11. [11]

    A Low Cost Underwater Robot with Grippers for Visual Inspection of External Pipeline Surface,

    M. Manjunatha, A. A. Selvakumar, V . P. Godeswar, and R. Manimaran, “A Low Cost Underwater Robot with Grippers for Visual Inspection of External Pipeline Surface,” Procedia computer science , vol. 133, pp. 108–115, 2018

    work page 2018

  12. [12]

    Buoyancy enabled au- tonomous underwater construction with cement blocks,

    S. Lensgraf, D. Balkcom, and A. Q. Li, “Buoyancy enabled au- tonomous underwater construction with cement blocks,” inIEEE Inter- national Conference on Robotics and Automation (ICRA) , pp. 5207– 5213, 2023

    work page 2023

  13. [13]

    Catching Jellies in Immersive Virtual Reality: A Comparative Teleoperation Study of ROVs in Underwater Capture Tasks,

    A. Elor, T. Thang, B. P. Hughes, A. Crosby, A. Phung, E. Gonzalez, K. Katija, S. H. Haddock, E. J. Martin, B. E. Erwin, et al., “Catching Jellies in Immersive Virtual Reality: A Comparative Teleoperation Study of ROVs in Underwater Capture Tasks,” in Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology , pp. 1–10, 2021

    work page 2021

  14. [14]

    Eye on the back: Augmented Visuals for Improved ROV Teleoperation in Deep Water Surveillance and Inspection,

    M. J. Islam, “Eye on the back: Augmented Visuals for Improved ROV Teleoperation in Deep Water Surveillance and Inspection,” in SPIE Defense and Commercial Sensing , (Maryland, USA), SPIE, 2024

    work page 2024

  15. [15]

    Three-dimensional Obstacle Avoidance for Autonomous Underwater Robot,

    W. Cai, Y . Wu, and M. Zhang, “Three-dimensional Obstacle Avoidance for Autonomous Underwater Robot,” IEEE Sensors Letters , vol. 4, no. 11, pp. 1–4, 2020

    work page 2020

  16. [16]

    Autonomous Exploration of Complex Underwater Environments using a Probabilis- tic Next-best-view Planner,

    N. Palomeras, N. Hurt ´os, E. Vidal, and M. Carreras, “Autonomous Exploration of Complex Underwater Environments using a Probabilis- tic Next-best-view Planner,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1619–1625, 2019

    work page 2019

  17. [17]

    Inspection Robot for Submarine Pipeline based on Ma- chine Vision,

    F. Yin, “Inspection Robot for Submarine Pipeline based on Ma- chine Vision,” in Journal of Physics: Conference Series , vol. 1952, p. 022034, IOP Publishing, 2021

    work page 1952

  18. [18]

    AquaVis: A Perception-aware Au- tonomous Navigation Framework for Underwater Vehicles,

    M. Xanthidis, M. Kalaitzakis, N. Karapetyan, J. Johnson, N. Vitzilaios, J. M. O’Kane, and I. Rekleitis, “AquaVis: A Perception-aware Au- tonomous Navigation Framework for Underwater Vehicles,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5410–5417, IEEE, 2021

    work page 2021

  19. [19]

    Mapping of Underwater Structures by a Team of Autonomous Underwater Vehicles,

    M. Xanthidis, B. Joshi, M. Roznere, W. Wang, N. Burgdorfer, A. Quattrini Li, P. Mordohai, S. Nelakuditi, and I. Rekleitis, “Mapping of Underwater Structures by a Team of Autonomous Underwater Vehicles,” in International Symposium of Robotics Research , 2022

    work page 2022

  20. [20]

    Surface-to-seabed Safety: Advantages of Simulator Practice for Subsea Installation,

    L. Vederhus and Y . Pan, “Surface-to-seabed Safety: Advantages of Simulator Practice for Subsea Installation,” International Journal of Safety and Security Engineering , vol. 6, no. 2, pp. 301–309, 2016

    work page 2016

  21. [21]

    Deep note: Can acoustic interference damage the availability of hard disk storage in underwater data centers?,

    J. Sheldon, W. Zhu, A. Abdullah, K. Butler, M. J. Islam, and S. Ram- pazzi, “Deep note: Can acoustic interference damage the availability of hard disk storage in underwater data centers?,” in 15th ACM Workshop on Hot Topics in Storage and File Systems. Best Paper Award., pp. 51– 57, 2023. Best Paper Award

    work page 2023

  22. [22]

    Subsea Fiber: Into the Deep,

    S. Curtis, “Subsea Fiber: Into the Deep,” Optics and Photonics News , vol. 34, no. 3, pp. 32–39, 2023

    work page 2023

  23. [23]

    Design and Testing of a Spherical Autonomous Underwater Vehicle for Shipwreck Interior Exploration,

    R. Eldred, J. Lussier, and A. Pollman, “Design and Testing of a Spherical Autonomous Underwater Vehicle for Shipwreck Interior Exploration,” Journal of Marine Science and Engineering , vol. 9, no. 3, p. 320, 2021

    work page 2021

  24. [24]

    Computer Vision Applications in Underwater Robotics and Oceanography,

    M. J. Islam, A. Q. Li, Y . A. Girdhar, and I. Rekleitis, “Computer Vision Applications in Underwater Robotics and Oceanography,” Computer Vision: Challenges, Trends, and Opportunities , pp. 173–196, 2024

    work page 2024

  25. [25]

    Deployable Wavelength Optimization for Free-space Communication Undersea,

    B. Neuner, B. M. Pascoguin, A. Hening, and B. Dick, “Deployable Wavelength Optimization for Free-space Communication Undersea,” in OCEANS 2016 MTS/IEEE Monterey , pp. 1–5, IEEE, 2016

    work page 2016

  26. [26]

    Real-time Dense 3d Mapping of Underwater Environments,

    W. Wang, B. Joshi, N. Burgdorfer, K. Batsosc, A. Q. Lid, P. Mordo- haia, and I. Rekleitisb, “Real-time Dense 3d Mapping of Underwater Environments,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , pp. 5184–5191, IEEE, 2023

    work page 2023

  27. [27]

    Envisioning Human- robot Coordination in Future Operations,

    D. Woods, J. Tittle, M. Feil, and A. Roesler, “Envisioning Human- robot Coordination in Future Operations,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , vol. 34, no. 2, pp. 210–218, 2004

    work page 2004

  28. [28]

    Human-robot Interactions during the Robot- assisted Urban Search and Rescue Response at the World Trade Center,

    J. Casper and R. Murphy, “Human-robot Interactions during the Robot- assisted Urban Search and Rescue Response at the World Trade Center,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 33, no. 3, pp. 367–385, 2003

    work page 2003

  29. [29]

    Flyar: Aug- mented Reality Supported Micro Aerial Vehicle Navigation,

    S. Zollmann, C. Hoppe, T. Langlotz, and G. Reitmayr, “Flyar: Aug- mented Reality Supported Micro Aerial Vehicle Navigation,” IEEE transactions on visualization and computer graphics , vol. 20, no. 4, pp. 560–568, 2014

    work page 2014

  30. [30]

    Look Closer: Bridging Egocentric and Third-Person Views With Transformers for Robotic Manipulation,

    R. Jangir, N. Hansen, S. Ghosal, M. Jain, and X. Wang, “Look Closer: Bridging Egocentric and Third-Person Views With Transformers for Robotic Manipulation,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3046–3053, 2022

    work page 2022

  31. [31]

    Aerial- Ground Collaborative Sensing: Third-Person View for Teleoperation,

    A. Gawel, Y . Lin, T. Koutros, R. Siegwart, and C. Cadena, “Aerial- Ground Collaborative Sensing: Third-Person View for Teleoperation,” in 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–7, 2018

    work page 2018

  32. [32]

    A Teleoperating Interface for Ground Vehicles using Autonomous Flying Cameras,

    D. Saakes, V . Choudhary, D. Sakamoto, M. Inami, and T. Lgarashi, “A Teleoperating Interface for Ground Vehicles using Autonomous Flying Cameras,” in 2013 23rd International Conference on Artificial Reality and Telexistence (ICAT) , pp. 13–19, 2013

    work page 2013

  33. [33]

    BirdViewAR: Surroundings-aware Remote Drone Piloting Using an Augmented Third-person Perspective,

    M. Inoue, K. Takashima, K. Fujita, and Y . Kitamura, “BirdViewAR: Surroundings-aware Remote Drone Piloting Using an Augmented Third-person Perspective,” inProceedings of the 2023 CHI Conference on Human Factors in Computing Systems , CHI ’23, (New York, NY , USA), Association for Computing Machinery, 2023

    work page 2023

  34. [34]

    Drone- augmented Human Vision: Exocentric Control for Drones Exploring Hidden Areas,

    O. Erat, W. A. Isop, D. Kalkofen, and D. Schmalstieg, “Drone- augmented Human Vision: Exocentric Control for Drones Exploring Hidden Areas,” IEEE transactions on visualization and computer graphics, vol. 24, no. 4, pp. 1437–1446, 2018

    work page 2018

  35. [35]

    Study on Effective Camera Images for Mobile Robot Teleoperation,

    N. Shiroma, N. Sato, Y .-h. Chiu, and F. Matsuno, “Study on Effective Camera Images for Mobile Robot Teleoperation,” in IEEE Interna- tional Workshop on Robot and Human Interactive Communication (RO-MAN), pp. 107–112, 2004

    work page 2004

  36. [36]

    Redesign of Rescue Mobile Robot Quince,

    K. Nagatani, S. Kiribayashi, Y . Okada, S. Tadokoro, T. Nishimura, T. Yoshida, E. Koyanagi, and Y . Hada, “Redesign of Rescue Mobile Robot Quince,” in 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics , pp. 13–18, 2011

    work page 2011

  37. [37]

    Spatio-temporal Bird’s-eye View Images using Multiple Fish-eye Cameras,

    T. Sato, A. Moro, A. Sugahara, T. Tasaki, A. Yamashita, and H. Asama, “Spatio-temporal Bird’s-eye View Images using Multiple Fish-eye Cameras,” in Proceedings of the 2013 IEEE/SICE International Sym- posium on System Integration , pp. 753–758, 2013

    work page 2013

  38. [38]

    Egocentric and exocentric teleoperation interface using real-time, 3d video projec- tion,

    F. Ferland, F. Pomerleau, C. T. Le Dinh, and F. Michaud, “Egocentric and exocentric teleoperation interface using real-time, 3d video projec- tion,” in Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction , HRI ’09, (New York, NY , USA), p. 37–44, Association for Computing Machinery, 2009

    work page 2009

  39. [39]

    Remote Operation of Unmanned Surface Vessel through Virtual Reality -A Low Cognitive Load Ap- proach,

    M. Lager, E. A. Topp, and J. Malec, “Remote Operation of Unmanned Surface Vessel through Virtual Reality -A Low Cognitive Load Ap- proach,” 03 2018

    work page 2018

  40. [40]

    Intuitive Robot Teleoperation Through Multi-Sensor Informed Mixed Reality Visual Aids,

    S. Livatino, D. C. Guastella, G. Muscato, V . Rinaldi, L. Cantelli, C. D. Melita, A. Caniglia, R. Mazza, and G. Padula, “Intuitive Robot Teleoperation Through Multi-Sensor Informed Mixed Reality Visual Aids,” IEEE Access, vol. 9, pp. 25795–25808, 2021

    work page 2021

  41. [41]

    Development and Evaluation of a Chase View for UA V Operations in Cluttered Environments,

    J. Hing, K. Sevcik, and P. Oh, “Development and Evaluation of a Chase View for UA V Operations in Cluttered Environments,” Journal of Intelligent and Robotic Systems , vol. 57, pp. 485–503, 08 2010

    work page 2010

  42. [42]

    A comparison of adaptive view techniques for exploratory 3d drone teleoperation,

    J. Thomason, P. Ratsamee, J. Orlosky, K. Kiyokawa, T. Mashita, Y . Uranishi, and H. Takemura, “A comparison of adaptive view techniques for exploratory 3d drone teleoperation,” ACM Transactions on Interactive Intelligent Systems , vol. 9, pp. 1–19, 2019

    work page 2019

  43. [43]

    Robot-to-robot Relative Pose Estimation using Humans as Markers,

    M. J. Islam, J. Mo, and J. Sattar, “Robot-to-robot Relative Pose Estimation using Humans as Markers,” Autonomous Robots, vol. 45, no. 4, pp. 579–593, 2021

    work page 2021

  44. [44]

    A teleoperation interface using past images for outdoor environment,

    M. Ito, N. Sato, M. Sugimoto, N. Shiroma, M. Inami, and F. Matsuno, “A teleoperation interface using past images for outdoor environment,” in 2008 SICE Annual Conference , pp. 3372–3375, IEEE, 2008

    work page 2008

  45. [45]

    Teleoperation System using Past Image Records for Mobile Manip- ulator,

    R. Murata, S. Songtong, H. Mizumoto, K. Kon, and F. Matsuno, “Teleoperation System using Past Image Records for Mobile Manip- ulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

    work page 2014

  46. [46]

    ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual- Inertial and Multi-Map SLAM,

    C. Campos, R. Elvira, J. J. G ´omez, J. M. M. Montiel, and J. D. Tard´os, “ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual- Inertial and Multi-Map SLAM,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021

    work page 2021

  47. [47]

    A Survey of State-of-the-art on Visual SLAM,

    I. A. Kazerouni, L. Fitzgerald, G. Dooly, and D. Toal, “A Survey of State-of-the-art on Visual SLAM,” Expert Systems with Applications , vol. 205, p. 117734, 2022

    work page 2022

  48. [48]

    A Comprehensive Survey of Visual SLAM Algorithms,

    A. Macario Barros, M. Michel, Y . Moline, G. Corre, and F. Carrel, “A Comprehensive Survey of Visual SLAM Algorithms,” Robotics, vol. 11, no. 1, p. 24, 2022

    work page 2022

  49. [49]

    CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration,

    A. Abdullah, T. Barua, R. Tibbetts, Z. Chen, M. J. Islam, and I. Rek- leitis, “CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration,” in IEEE International Conference on Robotics and Automation (ICRA) , IEEE, 2024

    work page 2024

  50. [50]

    AprilTag: A Robust and Flexible Visual Fiducial System,

    E. Olson, “AprilTag: A Robust and Flexible Visual Fiducial System,” in 2011 IEEE international conference on robotics and automation , pp. 3400–3407, IEEE, 2011

    work page 2011

  51. [51]

    SUS- A Quick and Dirty Usability Scale,

    J. Brooke et al., “SUS- A Quick and Dirty Usability Scale,” Usability Evaluation in Industry , vol. 189, no. 194, pp. 4–7, 1996

    work page 1996

  52. [52]

    Experimental Comparison of Open Source Visual- Inertial-Based State Estimation Algorithms in the Underwater Do- main,

    B. Joshi, S. Rahman, M. Kalaitzakis, B. Cain, J. Johnson, M. Xan- thidis, N. Karapetyan, A. Hernandez, A. Quattrini Li, N. Vitzilaios, and I. Rekleitis, “Experimental Comparison of Open Source Visual- Inertial-Based State Estimation Algorithms in the Underwater Do- main,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , (Mac...

    work page 2019

  53. [53]

    3d reconstruction of underwater scenes using nonlinear domain projection,

    J. Wu, B. Yu, and M. J. Islam, “3d reconstruction of underwater scenes using nonlinear domain projection,” in 2023 IEEE Conference on Artificial Intelligence (CAI) , pp. 359–361, IEEE, 2023. Best Paper Award

    work page 2023

  54. [54]

    SVIn2: A Multi-sensor Fusion-based Underwater SLAM System,

    S. Rahman, A. Quattrini Li, and I. Rekleitis, “SVIn2: A Multi-sensor Fusion-based Underwater SLAM System,” International Journal of Robotics Research, vol. 41, pp. 1022–1042, July 2022

    work page 2022

  55. [55]

    Fast direct stereo visual slam,

    J. Mo, M. J. Islam, and J. Sattar, “Fast direct stereo visual slam,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 778–785, 2021

    work page 2021