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arxiv: 2309.08780 · v2 · submitted 2023-09-15 · 💻 cs.RO

STERN: Simultaneous Trajectory Estimation and Relative Navigation for Autonomous Underwater Proximity Operations

Aldo Ter\'an Espinoza , Antonio Ter\'an Espinoza , John Folkesson , Clemens Deutsch , Niklas Rolleberg , Peter Sigray , Jakob Kuttenkeuler This is my paper

Pith reviewed 2026-05-24 06:34 UTC · model grok-4.3

classification 💻 cs.RO
keywords factor graphsunderwater proximity operationsrelative navigationtrajectory estimationautonomous underwater vehiclesacoustic homingmothership tracking
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The pith

Factor graphs model simultaneous trajectory estimation and relative navigation for any underwater proximity operation.

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

The paper establishes that factor graphs offer a single, flexible representation for the simultaneous trajectory estimation and relative navigation problem that arises in underwater proximity operations between a chaser vehicle and a target. This holds across different sensor types and vehicle motion constraints. A reader would care because underwater docking and similar tasks are limited by vehicle endurance, and a unified modeling approach could simplify estimator design for varied scenarios. The authors demonstrate the claim by recasting multiple published underwater proximity cases into the same graph structure and by implementing and testing an estimator on both simulated and real acoustic homing data to a moving mothership.

Core claim

Factor graphs serve as a generalized representation to model the underlying simultaneous trajectory estimation and relative navigation problem that arises with any proximity-operations scenario, regardless of the sensor suite or the agents' dynamic constraints.

What carries the argument

Factor graphs used as the modeling foundation that encodes measurements, motion models, and relative states into a single optimization problem for joint trajectory and navigation estimation.

If this is right

  • Any published proximity-operation scenario can be expressed with the identical factor-graph structure.
  • Estimator design and implementation for new underwater proximity tasks reduces to adding or removing factors without changing the overall solver.
  • Timing measurements confirm that the front- and back-end processes can run at rates suitable for real-time onboard deployment.
  • Limitations of the moving-target motion model can be quantified directly from the same graph by examining residual growth under real data.

Where Pith is reading between the lines

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

  • The same graph construction could be reused for surface or aerial proximity tasks if the measurement factors are swapped accordingly.
  • Standardized factor libraries would let teams swap sensor suites while keeping the navigation solution architecture unchanged.
  • Extending the graph to include explicit docking-phase constraints would allow a single estimator to handle the full sequence from long-range homing to final contact.

Load-bearing premise

The dynamic assumptions made for the moving target are sufficiently accurate and flexible for the long-distance acoustic homing scenario.

What would settle it

A field trial in which the mothership executes maneuvers outside the modeled dynamics and the resulting estimator produces relative-position errors that exceed the accuracy needed for docking.

Figures

Figures reproduced from arXiv: 2309.08780 by Aldo Ter\'an Espinoza, Antonio Ter\'an Espinoza, Clemens Deutsch, Jakob Kuttenkeuler, John Folkesson, Niklas Rolleberg, Peter Sigray.

Figure 1
Figure 1. Figure 1: Definition of the prox-ops phases established to generalize any [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Factor graph representation of a general proximity operation scenario. As the chaser navigates through the different phases of the prox-ops scenario [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Side-by-side representation of the prox-ops scenario addressed in the present work. Left: pictorial description of the chasing AUV homing into the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reference frames involved in the USBL measurement procedure. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Pictorial description of the dynamic USBL measurement scenario. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Block diagram showing the high-level module description and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: RANSAC-based outlier rejection example. New USBL measurements [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Resulting 2D trajectories, depths, and absolute errors of the simulated [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Images of the setup used for experimental verification: a) shows the side-deployment pole with the AHRS and differential GPS at mounted on top, [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Exploded view of the labeled paths executed by the target (red) and [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Plots showcasing the results obtained after processing the Test 1 dataset. The figure shows the global trajectories (true, real-time estimated, smoothed, [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Plots for the absolute position, velocity [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Error bars representing the mean (black circle) and standard deviation [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
read the original abstract

Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of relative navigation for the generalized version of the docking operation, which we address as proximity operations. Proximity operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We emphasize the versatility on the use of factor graphs as a generalized representation to model the underlying simultaneous trajectory estimation and relative navigation (STERN) problem that arises with any prox-ops scenario, regardless of the sensor suite or the agents' dynamic constraints. To emphasize the flexibility of factor graphs as the modeling foundation for arbitrary underwater prox-ops, we compile a list of state-of-the-art research in the field and represent the different scenario using the same factor graph representation. We detail the procedure required to model, design, and implement factor graph-based estimators by addressing a long-distance acoustic homing scenario of an AUV to a moving mothership using datasets from simulated and real-world deployments; an analysis of these results is provided to shed light on the flexibility and limitations of the dynamic assumptions of the moving target. A description of our front- and back-end is also presented together with a timing breakdown of all processes to show its potential deployment on a real-time system.

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 paper introduces STERN, a factor graph-based approach for simultaneous trajectory estimation and relative navigation in underwater proximity operations between an AUV (chaser) and a target (mothership). It frames docking as prox-ops phases, emphasizes the versatility of factor graphs for any scenario regardless of sensors or dynamics, maps SOTA research to the same FG structure, and details implementation for long-distance acoustic homing using simulated and real datasets, with analysis of dynamic assumption limitations and timing for real-time potential.

Significance. If the generality claim holds, this provides a unified, modular framework for underwater AUV prox-ops that could facilitate adaptation across different scenarios and sensor suites. The use of both simulated and real-world data, along with the compilation of multiple SOTA scenarios into one representation, strengthens the case for factor graphs as a flexible modeling tool in this domain.

major comments (1)
  1. [Abstract] Abstract: the central claim that factor graphs model the STERN problem 'regardless of the sensor suite or the agents' dynamic constraints' is load-bearing for the contribution but rests on the premise that the dynamic factors chosen for the mothership in the acoustic homing implementation remain adequate across qualitatively different target dynamics; the limitation analysis must explicitly demonstrate (via additional mappings or tests) that no new factor types are required to instantiate the same template for alternative motion models.
minor comments (1)
  1. [Abstract] Abstract: results from simulated and real datasets are mentioned but no error metrics, quantitative comparisons, or dataset details are provided; these should be summarized to allow assessment of the generality claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the potential of the factor-graph framework. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that factor graphs model the STERN problem 'regardless of the sensor suite or the agents' dynamic constraints' is load-bearing for the contribution but rests on the premise that the dynamic factors chosen for the mothership in the acoustic homing implementation remain adequate across qualitatively different target dynamics; the limitation analysis must explicitly demonstrate (via additional mappings or tests) that no new factor types are required to instantiate the same template for alternative motion models.

    Authors: We thank the referee for isolating this load-bearing claim. The manuscript already maps multiple SOTA scenarios that employ qualitatively different target dynamics (stationary docking stations, constant-velocity motherships, and maneuvering targets) onto the identical factor-graph template using only standard factor types (priors, relative-pose, odometry, and range-bearing factors). These mappings demonstrate that the template itself does not change; only the choice of dynamic factor within the template is adapted to the motion model. The existing limitation analysis discusses the adequacy of the constant-velocity assumption for the acoustic-homing case but does not repeat the cross-scenario mappings. We will therefore revise the limitation-analysis section to explicitly restate the SOTA mappings and add one or two additional illustrative instantiations (e.g., a highly maneuverable target modeled with an acceleration factor) to confirm that no new factor types are introduced. This revision will be included in the next version. revision: yes

Circularity Check

0 steps flagged

No significant circularity; factor-graph modeling is an independent representational choice

full rationale

The paper presents factor graphs as a modeling framework applied to the STERN problem for arbitrary prox-ops scenarios. It compiles existing SOTA work into the same representation and demonstrates one acoustic-homing implementation while noting limitations of the mothership motion priors. No load-bearing step reduces by the paper's own equations or self-citation to a fitted parameter or definition that is equivalent to the claimed result; the generality assertion is an application of a standard tool rather than a self-referential derivation. The central modeling choice remains independent of the specific dynamic assumptions used in the example.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text. The central modeling decision is the choice of factor graphs as the representation.

axioms (1)
  • domain assumption Dynamic assumptions for the moving target hold with acceptable flexibility for the acoustic homing scenario.
    Abstract states that results analysis will illuminate limitations of these assumptions, implying they are load-bearing for the reported evaluation.

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Reference graph

Works this paper leans on

74 extracted references · 74 canonical work pages

  1. [1]

    Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience,

    R. B. Wynn, V . A. I. Huvenne, T. P. Le Bas, B. J. Murton, D. P. Connelly, B. J. Bett, H. A. Ruhl, K. J. Morris, J. Peakall, D. R. Parsons, E. J. Sumner, S. E. Darby, R. M. Dorrell, and J. E. Hunt, “Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience,” Marine Geology, vol. 352, pp. 45...

    work page 2014

  2. [2]

    Underwater docking of autonomous undersea vehicles using optical terminal guidance,

    S. Cowen, S. Briest, and J. Dombrowski, “Underwater docking of autonomous undersea vehicles using optical terminal guidance,” in Oceans ’97. MTS/IEEE Conference Proceedings, vol. 2, pp. 1143–1147 vol.2, Oct. 1997

    work page 1997

  3. [3]

    Investigation of autonomous docking strategies for robotic operation on intervention panels,

    S. Krupinski, F. Maurelli, G. Grenon, and Y . Petillot, “Investigation of autonomous docking strategies for robotic operation on intervention panels,” in OCEANS 2008, pp. 1–10, Sept. 2008. ISSN: 0197-7385

    work page 2008

  4. [4]

    Three-dimensional mapping of marine caves using a handheld echosounder,

    V . Gerovasileiou, V . Trygonis, M. Sini, D. Koutsoubas, and E. V oult- siadou, “Three-dimensional mapping of marine caves using a handheld echosounder,”Marine Ecology Progress Series, vol. 486, pp. 13–22, July 2013

    work page 2013

  5. [5]

    Diver Depth-Gauge Profiling beyond Wading Depths: A New Simple Method for Underwater Survey- ing,

    J. L. Rahn, H. J. Lannon, and J. Mossa, “Diver Depth-Gauge Profiling beyond Wading Depths: A New Simple Method for Underwater Survey- ing,” Journal of Coastal Research , vol. 300, pp. 505–511, Mar. 2015

    work page 2015

  6. [6]

    Overseas trends in the development of human occupied deep submersibles and a proposal for japan’s way to take,

    K. Kudo, “Overseas trends in the development of human occupied deep submersibles and a proposal for japan’s way to take,” tech. rep., Citeseer, 2008

    work page 2008

  7. [7]

    Challenges and future trends in marine robotics,

    E. Zereik, M. Bibuli, N. Mi ˇskovi´c, P. Ridao, and A. Pascoal, “Challenges and future trends in marine robotics,”Annual Reviews in Control, vol. 46, pp. 350–368, 2018

    work page 2018

  8. [8]

    An Arctic Basin Observational Capa- bility Using AUVs,

    J. Bellingham, K. Streitlien, J. Overland, S. Rajah, P. Stein, J. Stannard, W. Kirkwood, and D. Yoerger, “An Arctic Basin Observational Capa- bility Using AUVs,” Oceanography, vol. 13, no. 2, pp. 64–70, 2000

    work page 2000

  9. [9]

    12 days under ice – an historic AUV deployment in the Canadian High Arctic,

    C. Kaminski, T. Crees, J. Ferguson, A. Forrest, J. Williams, D. Hopkin, and G. Heard, “12 days under ice – an historic AUV deployment in the Canadian High Arctic,” in 2010 IEEE/OES Autonomous Underwater Vehicles, pp. 1–11, Sept. 2010. ISSN: 2377-6536

    work page 2010

  10. [10]

    Toward Autonomous Exploration in Confined Underwater Environments,

    A. Mallios, P. Ridao, D. Ribas, M. Carreras, and R. Camilli, “Toward Autonomous Exploration in Confined Underwater Environments,” Jour- nal of Field Robotics , vol. 33, no. 7, pp. 994–1012, 2016

    work page 2016

  11. [11]

    Underwater Vehicle Manipulators,

    T. W. Kim, G. Marani, and J. Yuh, “Underwater Vehicle Manipulators,” in Springer Handbook of Ocean Engineering (M. R. Dhanak and N. I. Xiros, eds.), Springer Handbooks, pp. 407–422, Cham: Springer International Publishing, 2016

    work page 2016

  12. [12]

    Intervention AUVs: The next challenge,

    P. Ridao, M. Carreras, D. Ribas, P. J. Sanz, and G. Oliver, “Intervention AUVs: The next challenge,” Annual Reviews in Control , vol. 40, pp. 227–241, Jan. 2015

    work page 2015

  13. [13]

    Autonomous and Remotely Operated Vehicle Technology for Hy- drothermal Vent Discovery, Exploration, and Sampling,

    D. Yoerger, A. Bradley, M. Jakuba, C. German, T. Shank, and M. Tivey, “Autonomous and Remotely Operated Vehicle Technology for Hy- drothermal Vent Discovery, Exploration, and Sampling,”Oceanography, vol. 20, pp. 152–161, Mar. 2007

    work page 2007

  14. [14]

    ROVs and AUVs,

    V . A. Huvenne, K. Robert, L. Marsh, C. Lo Iacono, T. Le Bas, and R. B. Wynn, “ROVs and AUVs,” in Submarine Geomorphology (A. Micallef, S. Krastel, and A. Savini, eds.), Springer Geology, pp. 93–108, Cham: Springer International Publishing, 2018

    work page 2018

  15. [15]

    Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle,

    S. B. Williams, O. R. Pizarro, M. V . Jakuba, C. R. Johnson, N. S. Barrett, R. C. Babcock, G. A. Kendrick, P. D. Steinberg, A. J. Heyward, P. J. Doherty, I. Mahon, M. Johnson-Roberson, D. Steinberg, and A. Fried- man, “Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle,” IEEE Robotics & Automation Magazine , vol. 19, pp. 73–84, ...

    work page 2012

  16. [16]

    Subsea infrastructure inspection: A review study,

    C. Mai, S. Pedersen, L. Hansen, K. L. Jepsen, and Z. Yang, “Subsea infrastructure inspection: A review study,” in 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS), pp. 71–76, Dec. 2016

    work page 2016

  17. [17]

    Vision based au- tonomous docking for work class ROVs,

    P. Trslic, M. Rossi, L. Robinson, C. W. O’Donnel, A. Weir, J. Coleman, J. Riordan, E. Omerdic, G. Dooly, and D. Toal, “Vision based au- tonomous docking for work class ROVs,” Ocean Engineering, vol. 196, p. 106840, Jan. 2020

    work page 2020

  18. [18]

    A survey of underwater docking guidance systems,

    A. Yazdani, K. Sammut, O. Yakimenko, and A. Lammas, “A survey of underwater docking guidance systems,” Robotics and Autonomous Systems, vol. 124, p. 103382, Feb. 2020

    work page 2020

  19. [19]

    End-to-End Framework for Close Proximity In-Space Robotic Missions,

    A. Teran Espinoza, H. Hettrick, K. Albee, A. Cabrales Hernandez, and R. Linares, “End-to-End Framework for Close Proximity In-Space Robotic Missions,” Oct. 2019

    work page 2019

  20. [20]

    Advances in inference and representation for simultaneous localization and mapping,

    D. M. Rosen, K. J. Doherty, A. Ter ´an Espinoza, and J. J. Leonard, “Advances in inference and representation for simultaneous localization and mapping,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 4, pp. 215–242, 2021

    work page 2021

  21. [21]

    Autonomous Underwater Vehicle Docking,

    J. G. Bellingham, “Autonomous Underwater Vehicle Docking,” in Springer Handbook of Ocean Engineering (M. R. Dhanak and N. I. Xiros, eds.), pp. 387–406, Cham: Springer International Publishing, 2016

    work page 2016

  22. [22]

    Factor Graphs for Robot Perception,

    F. Dellaert and M. Kaess, “Factor Graphs for Robot Perception,” Foundations and Trends® in Robotics , vol. 6, pp. 1–139, Aug. 2017. Publisher: Now Publishers, Inc

    work page 2017

  23. [23]

    Koller and N

    D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques. MIT press, 2009

    work page 2009

  24. [24]

    Au- tonomous underwater vehicle homing/docking via electromagnetic guid- ance,

    M. Feezor, F. Yates Sorrell, P. Blankinship, and J. Bellingham, “Au- tonomous underwater vehicle homing/docking via electromagnetic guid- ance,” IEEE Journal of Oceanic Engineering, vol. 26, pp. 515–521, Oct

    work page

  25. [26]

    Underwater Docking Approach and Homing to Enable Persistent Op- eration,

    B. R. Page, R. Lambert, J. Chavez-Galaviz, and N. Mahmoudian, “Underwater Docking Approach and Homing to Enable Persistent Op- eration,” Frontiers in Robotics and AI , vol. 8, 2021

    work page 2021

  26. [27]

    Docking to an underwater suspended charging station: Systematic design and experimental tests,

    M. Lin, R. Lin, C. Yang, D. Li, Z. Zhang, Y . Zhao, and W. Ding, “Docking to an underwater suspended charging station: Systematic design and experimental tests,” Ocean Engineering, vol. 249, p. 110766, Apr. 2022

    work page 2022

  27. [28]

    From the lab to the ocean: Characterizing the critical docking parameters for a free floating dock with a REMUS 600,

    B. Fletcher, S. Martin, G. Flores, A. Jones, A. Nguyen, M. H. Brown, and D. L. Moore, “From the lab to the ocean: Characterizing the critical docking parameters for a free floating dock with a REMUS 600,” in OCEANS 2017 - Anchorage , pp. 1–7, Sept. 2017

    work page 2017

  28. [29]

    Detection and Pose Estimation for Short-Range Vision-Based Underwater Docking,

    S. Liu, M. Ozay, T. Okatani, H. Xu, K. Sun, and Y . Lin, “Detection and Pose Estimation for Short-Range Vision-Based Underwater Docking,” IEEE Access , vol. 7, pp. 2720–2749, 2019. Conference Name: IEEE Access

    work page 2019

  29. [30]

    An integrated approach to multiple AUV communications, navigation and docking,

    H. Singh, J. Catipovic, R. Eastwood, L. Freitag, H. Henriksen, F. Hover, D. Yoerger, J. Bellingham, and B. Moran, “An integrated approach to multiple AUV communications, navigation and docking,” in OCEANS 96 MTS/IEEE Conference Proceedings. The Coastal Ocean - Prospects for the 21st Century , vol. 1, pp. 59–64 vol.1, Sept. 1996

    work page 1996

  30. [31]

    Launch and Recovery of an Autonomous Underwater Vehicle From a Station-Keeping Unmanned Surface Vehi- cle,

    E. I. Sarda and M. R. Dhanak, “Launch and Recovery of an Autonomous Underwater Vehicle From a Station-Keeping Unmanned Surface Vehi- cle,” IEEE Journal of Oceanic Engineering , vol. 44, pp. 290–299, Apr

    work page

  31. [32]

    Conference Name: IEEE Journal of Oceanic Engineering

    work page

  32. [33]

    Homing a robot with range-only measurements under unknown drifts,

    B. M. Ferreira, A. C. Matos, N. A. Cruz, and A. P. Moreira, “Homing a robot with range-only measurements under unknown drifts,” Robotics and Autonomous Systems , vol. 67, pp. 3–13, May 2015

    work page 2015

  33. [34]

    Docking Control System for a 54-cm-Diameter (21-in) AUV,

    R. S. McEwen, B. W. Hobson, L. McBride, and J. G. Bellingham, “Docking Control System for a 54-cm-Diameter (21-in) AUV,” IEEE Journal of Oceanic Engineering , vol. 33, pp. 550–562, Oct. 2008. Conference Name: IEEE Journal of Oceanic Engineering

    work page 2008

  34. [35]

    Underwater Electro- magnetic Guidance Based on the Magnetic Dipole Model Applied in AUV Terminal Docking,

    R. Lin, Y . Zhao, D. Li, M. Lin, and C. Yang, “Underwater Electro- magnetic Guidance Based on the Magnetic Dipole Model Applied in AUV Terminal Docking,” Journal of Marine Science and Engineering , vol. 10, p. 995, July 2022

    work page 2022

  35. [36]

    Concept and testing of an electromagnetic homing guidance system for autonomous underwater vehicles,

    B. N. J. Vandavasi, U. Arunachalam, V . Narayanaswamy, R. Raju, D. P. Vittal, R. Muthiah, and A. R. Gidugu, “Concept and testing of an electromagnetic homing guidance system for autonomous underwater vehicles,” Applied Ocean Research , vol. 73, pp. 149–159, Apr. 2018

    work page 2018

  36. [37]

    Passive acoustic and optical guidance for underwater vehicles,

    H. Kondo, K. Okayama, J.-K. Choi, T. Hotta, M. Kondo, T. Okazaki, H. Singh, Z. Chao, K. Nitadori, M. Igarashi, and T. Fukuchi, “Passive acoustic and optical guidance for underwater vehicles,” in 2012 Oceans - Yeosu, pp. 1–6, May 2012

    work page 2012

  37. [38]

    AUV docking experiments based on vision positioning using two cameras,

    Y . Li, Y . Jiang, J. Cao, B. Wang, and Y . Li, “AUV docking experiments based on vision positioning using two cameras,” Ocean Engineering , vol. 110, pp. 163–173, Dec. 2015

    work page 2015

  38. [39]

    Experiments on vision guided docking of an autonomous underwater vehicle using one camera,

    J.-Y . Park, B.-h. Jun, P.-m. Lee, and J. Oh, “Experiments on vision guided docking of an autonomous underwater vehicle using one camera,” Ocean Engineering, vol. 36, pp. 48–61, Jan. 2009

    work page 2009

  39. [40]

    Terminal Stage Guidance Method for Underwater Moving Rendezvous and Docking Based on Monocular Vision,

    L. Zhang, Y . Li, G. Pan, Y . Zhang, and S. Li, “Terminal Stage Guidance Method for Underwater Moving Rendezvous and Docking Based on Monocular Vision,” in OCEANS 2019 - Marseille , pp. 1–6, June 2019

    work page 2019

  40. [41]

    Dynamic Docking Technology between AUV and Mobile Mothership,

    Z. Yan, P. Gong, and W. Zhang, “Dynamic Docking Technology between AUV and Mobile Mothership,” in 2020 Chinese Control And Decision Conference (CCDC), pp. 3045–3049, Aug. 2020. ISSN: 1948-9447

    work page 2020

  41. [42]

    AUV docking based on USBL navigation and vision guidance,

    S. Fan, C. Liu, B. Li, Y . Xu, and W. Xu, “AUV docking based on USBL navigation and vision guidance,” Journal of Marine Science and Technology, vol. 24, pp. 673–685, Sept. 2019

    work page 2019

  42. [43]

    Au- tonomous docking for Intervention-AUVs using sonar and video-based real-time 3D pose estimation,

    J. Evans, P. Redmond, C. Plakas, K. Hamilton, and D. Lane, “Au- tonomous docking for Intervention-AUVs using sonar and video-based real-time 3D pose estimation,” in Oceans 2003. Celebrating the Past 18 ... Teaming Toward the Future (IEEE Cat. No.03CH37492) , vol. 4, pp. 2201–2210 V ol.4, Sept. 2003

    work page 2003

  43. [44]

    The ARTEMIS under-ice AUV dock- ing system,

    P. W. Kimball, E. B. Clark, M. Scully, K. Richmond, C. Flesher, L. E. Lindzey, J. Harman, K. Huffstutler, J. Lawrence, S. Lelievre, J. Moor, B. Pease, V . Siegel, L. Winslow, D. D. Blankenship, P. Doran, S. Kim, B. E. Schmidt, and W. C. Stone, “The ARTEMIS under-ice AUV dock- ing system,”Journal of Field Robotics, vol. 35, no. 2, pp. 299–308, 2018. eprint...

    work page doi:10.1002/rob.21740 2018

  44. [45]

    Resident au- tonomous underwater vehicle: Underwater system for prolonged and continuous monitoring based at a seafloor station,

    T. Matsuda, T. Maki, K. Masuda, and T. Sakamaki, “Resident au- tonomous underwater vehicle: Underwater system for prolonged and continuous monitoring based at a seafloor station,” Robotics and Au- tonomous Systems, vol. 120, p. 103231, Oct. 2019

    work page 2019

  45. [46]

    Docking Method for Hovering-Type AUVs Based on Acoustic and Optical Landmarks,

    T. Maki, Y . Sato, T. Matsuda, K. Masuda, and T. Sakamaki, “Docking Method for Hovering-Type AUVs Based on Acoustic and Optical Landmarks,” Journal of Robotics and Mechatronics , vol. 30, pp. 55– 64, Feb. 2018. Publisher: Fuji Technology Press Ltd

    work page 2018

  46. [47]

    Autonomous homing and docking for AUVs using Range- Only Localization and Light Beacons,

    G. Vallicrosa, J. Bosch, N. Palomeras, P. Ridao, M. Carreras, and N. Gracias, “Autonomous homing and docking for AUVs using Range- Only Localization and Light Beacons,” IFAC-PapersOnLine, vol. 49, no. 23, pp. 54–60, 2016

    work page 2016

  47. [48]

    LOON-DOCK: AUV homing and docking for high-bandwidth data transmission,

    N. Hurt ´os, A. Mallios, N. Palomeras, J. Bosch, G. Vallicrosa, E. Vidal, D. Ribas, N. Gracias, M. Carreras, and P. Ridao, “LOON-DOCK: AUV homing and docking for high-bandwidth data transmission,” in OCEANS 2017 - Aberdeen , pp. 1–7, June 2017

    work page 2017

  48. [49]

    A single acoustic beacon-based positioning method for underwater mobile recovery of an AUV,

    Z. Zhou, Y . Jiang, Y . Li, C. Jian, and Y . Sun, “A single acoustic beacon-based positioning method for underwater mobile recovery of an AUV,” International Journal of Advanced Robotic Systems , vol. 15, p. 172988141880173, Sept. 2018

    work page 2018

  49. [50]

    Autonomous I- AUV Docking for Fixed-base Manipulation,

    N. Palomeras, P. Ridao, D. Ribas, and G. Vallicrosa, “Autonomous I- AUV Docking for Fixed-base Manipulation,” IFAC Proceedings Vol- umes, vol. 47, pp. 12160–12165, Jan. 2014

    work page 2014

  50. [51]

    A fully autonomous docking strategy for Intervention AUVs,

    L. Brignone, M. Perrier, and C. Viala, “A fully autonomous docking strategy for Intervention AUVs,” in OCEANS 2007 - Europe , pp. 1–6, June 2007

    work page 2007

  51. [52]

    Vision based localization system for AUV docking on subsea intervention panels,

    T. Palmer, D. Ribas, P. Ridao, and A. Mallios, “Vision based localization system for AUV docking on subsea intervention panels,” in OCEANS 2009-EUROPE, pp. 1–10, May 2009

    work page 2009

  52. [53]

    Relocat- ing underwater features autonomously using sonar-based slam,

    M. F. Fallon, J. Folkesson, H. McClelland, and J. J. Leonard, “Relocat- ing underwater features autonomously using sonar-based slam,” IEEE Journal of Oceanic Engineering , vol. 38, no. 3, pp. 500–513, 2013

    work page 2013

  53. [54]

    Navigation and Probability Assessment for Successful AUV Docking Using USBL,

    A. Sans-Muntadas, E. F. Brekke, Ø. Hegrenaes, and K. Y . Pettersen, “Navigation and Probability Assessment for Successful AUV Docking Using USBL,” IFAC-PapersOnLine, vol. 48, no. 16, pp. 204–209, 2015

    work page 2015

  54. [55]

    Homing by acoustic ranging to a single beacon,

    J. Vaganay, P. Baccou, and B. Jouvencel, “Homing by acoustic ranging to a single beacon,” in OCEANS 2000 MTS/IEEE Conference and Exhi- bition. Conference Proceedings (Cat. No.00CH37158), vol. 2, pp. 1457– 1462 vol.2, Sept. 2000

    work page 2000

  55. [56]

    A Robust Fuzzy Autonomous Underwater Vehicle (AUV) Docking Approach for Unknown Current Disturbances,

    K. Teo, E. An, and P.-P. J. Beaujean, “A Robust Fuzzy Autonomous Underwater Vehicle (AUV) Docking Approach for Unknown Current Disturbances,” IEEE Journal of Oceanic Engineering , vol. 37, pp. 143– 155, Apr. 2012. Conference Name: IEEE Journal of Oceanic Engineer- ing

    work page 2012

  56. [57]

    Autonomous Underwater Vehicle Homing With a Single Range-Only Beacon,

    J. R. Keane, A. L. Forrest, H. Johannsson, and D. Battle, “Autonomous Underwater Vehicle Homing With a Single Range-Only Beacon,” IEEE Journal of Oceanic Engineering , vol. 45, pp. 395–403, Apr. 2020. Conference Name: IEEE Journal of Oceanic Engineering

    work page 2020

  57. [58]

    An Improved Localization Method for the Transition between Autonomous Under- water Vehicle Homing and Docking,

    R. Lin, F. Zhang, D. Li, M. Lin, G. Zhou, and C. Yang, “An Improved Localization Method for the Transition between Autonomous Under- water Vehicle Homing and Docking,” Sensors, vol. 21, p. 2468, Apr. 2021

    work page 2021

  58. [59]

    Visual navigation and docking for a planar type AUV docking and charging system,

    T. Wang, Q. Zhao, and C. Yang, “Visual navigation and docking for a planar type AUV docking and charging system,” Ocean Engineering , vol. 224, p. 108744, Mar. 2021

    work page 2021

  59. [60]

    A Factor Graph Method for AUV Navigation in the Mobile Docking Progress,

    L. Ruan, S. Chen, J. Zhou, D. Zeng, and Y . Xu, “A Factor Graph Method for AUV Navigation in the Mobile Docking Progress,” inGlobal Oceans 2020: Singapore – U.S. Gulf Coast , pp. 1–5, Oct. 2020. ISSN: 0197- 7385

    work page 2020

  60. [61]

    Underwater mobile docking of autonomous underwater vehi- cles,

    Hydroid, “Underwater mobile docking of autonomous underwater vehi- cles,” in 2012 Oceans, pp. 1–15, Oct. 2012. ISSN: 0197-7385

    work page 2012

  61. [62]

    A Concept for Docking a UUV With a Slowly Moving Submarine Under Waves,

    G. D. Watt, A. R. Roy, J. Currie, C. B. Gillis, J. Giesbrecht, G. J. Heard, M. Birsan, M. L. Seto, J. A. Carretero, R. Dubay, and T. L. Jeans, “A Concept for Docking a UUV With a Slowly Moving Submarine Under Waves,” IEEE Journal of Oceanic Engineering , vol. 41, pp. 471–498, Apr. 2016. Conference Name: IEEE Journal of Oceanic Engineering

    work page 2016

  62. [63]

    Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors,

    L. Carlone, Z. Kira, C. Beall, V . Indelman, and F. Dellaert, “Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) , pp. 4290–4297, May 2014. ISSN: 1050-4729

    work page 2014

  63. [64]

    IMU Preinte- gration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation,

    C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, “IMU Preinte- gration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation,” 2015. Accepted: 2016-07-29T17:49:42Z Publisher: Georgia Institute of Technology

    work page 2015

  64. [65]

    Autonomous Underwater Vehicle Naviga- tion,

    J. J. Leonard and A. Bahr, “Autonomous Underwater Vehicle Naviga- tion,” in Springer Handbook of Ocean Engineering (M. R. Dhanak and N. I. Xiros, eds.), pp. 341–358, Cham: Springer International Publishing, 2016

    work page 2016

  65. [66]

    Integrating generic sensor fusion algorithms with sound state representations through encap- sulation of manifolds,

    C. Hertzberg, R. Wagner, U. Frese, and L. Schr ¨oder, “Integrating generic sensor fusion algorithms with sound state representations through encap- sulation of manifolds,” Information Fusion , vol. 14, no. 1, pp. 57–77, 2013

    work page 2013

  66. [67]

    Ros: an open-source robot operating system,

    M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y . Ng, et al. , “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, p. 5, Kobe, Japan, 2009

    work page 2009

  67. [68]

    borglab/gtsam,

    F. Dellaert and G. Contributors, “borglab/gtsam,” May 2022

    work page 2022

  68. [69]

    Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,

    M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, pp. 381–395, June 1981

    work page 1981

  69. [70]

    Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes,

    J. Rehder, J. Nikolic, T. Schneider, T. Hinzmann, and R. Siegwart, “Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) , pp. 4304–4311, IEEE, 2016

    work page 2016

  70. [71]

    isam2: Incremental smoothing and mapping using the bayes tree,

    M. Kaess, H. Johannsson, R. Roberts, V . Ila, J. J. Leonard, and F. Dellaert, “isam2: Incremental smoothing and mapping using the bayes tree,” The International Journal of Robotics Research , vol. 31, no. 2, pp. 216–235, 2012

    work page 2012

  71. [72]

    Stonefish: An Advanced Open-Source Simulation Tool Designed for Marine Robotics, With a ROS Interface,

    P. Cie ´slak, “Stonefish: An Advanced Open-Source Simulation Tool Designed for Marine Robotics, With a ROS Interface,” inOCEANS 2019 - Marseille, pp. 1–6, June 2019

    work page 2019

  72. [73]

    A system for au- tonomous seaweed farm inspection with an underwater robot,

    I. Stenius, J. Folkesson, S. Bhat, C. I. Sprague, L. Ling, ¨O. ¨Ozkahraman, N. Bore, Z. Cong, J. Severholt, C. Ljung, et al. , “A system for au- tonomous seaweed farm inspection with an underwater robot,” Sensors, vol. 22, no. 13, p. 5064, 2022

    work page 2022

  73. [74]

    Design of an AUV Research Platform for Demonstration of Novel Technologies,

    C. Deutsch, L. Moratelli, S. Thun ´e, J. Kuttenkeuler, and F. S ¨oderling, “Design of an AUV Research Platform for Demonstration of Novel Technologies,” in 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), pp. 1–8, Nov. 2018. ISSN: 2377-6536

    work page 2018

  74. [75]

    Smarc swedish maritime robotics centre : Mid-term evaluation report 2020,

    I. Stenius, P. Sigray, and L. Gunnar, “Smarc swedish maritime robotics centre : Mid-term evaluation report 2020,” Tech. Rep. 2020:006, 2020. QC 20200930. Aldo Ter ´an Espinoza received the B.S. de- gree in automotive engineering from the Instituto Polit´ecnico Nacional in Mexico City, Mexico, in 2017, and the M.Sc. degree in computer science and engineeri...

    work page 2020