pith. sign in

arxiv: 2606.06358 · v1 · pith:F6S6OFRM · submitted 2026-06-04 · eess.SY · cs.SY· eess.SP

Impact of RTK Augmentation and INS Integration on GNSS Positioning Accuracy and Continuity: A Benchmarking Study on Inland Waterways

Yan-Yun Zhang , Jef Billet , Jan Swevers , Peter Slaets This is my paper

pith:F6S6OFRMreviewed 2026-06-28 00:05 UTCmodel grok-4.3open to challenge →

classification eess.SY cs.SYeess.SP
keywords GNSS positioningRTK augmentationINS integrationinland waterwayspositioning accuracypositioning continuitybridge passagesensor benchmarking
0
0 comments X

The pith

RTK augmentation substantially improves GNSS positioning precision and uncertainty consistency on inland waterways while INS integration supports short-term continuity during outages but can introduce drift or jumps.

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

The paper benchmarks four GNSS receiver configurations on inland waterways to assess RTK augmentation and INS integration effects. Static tests and closed-loop path-following experiments during bridge passages demonstrate that RTK corrections reduce positioning errors and produce more consistent uncertainty estimates. INS integration helps bridge short gaps in RTK availability but risks position drift, bias, or recovery-induced jumps exceeding one meter. These results indicate that nominal receiver specifications fall short and that deployment-specific testing is essential for reliable performance. The work concludes that higher-level state estimation is needed next to achieve continuous and uncertainty-consistent positioning.

Core claim

Static benchmarking and closed-loop experiments confirm that RTK augmentation substantially improves positioning precision and uncertainty consistency, while INS integration supports short-term continuity during RTK unavailability but may introduce drift, bias, or transient uncertainty variations.

What carries the argument

Benchmarking of four receiver configurations (standalone GNSS, standalone GNSS with INS, RTK-augmented GNSS, RTK-augmented GNSS with INS) through static evaluation and closed-loop path-following experiments on an AsteRx-i3 D Pro+ receiver during real bridge passages.

If this is right

  • RTK correction loss during bridge passage reduces positioning accuracy, increases uncertainty, and triggers recovery state jumps exceeding 1 meter.
  • INS integration maintains short-term continuity when RTK is unavailable but can add drift, bias, or transient uncertainty variations.
  • Nominal receiver specifications are insufficient, requiring deployment-specific characterization for inland waterway use.
  • Higher-level state estimation is a necessary next step for spatially continuous and uncertainty-consistent positioning.

Where Pith is reading between the lines

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

  • The same trade-offs between precision and continuity may appear in other GNSS-denied settings such as urban areas or tunnels.
  • The publicly released experimental dataset could support development and testing of improved fusion methods.
  • Autonomous vessels may need supplementary sensors to limit INS drift accumulation over longer outages.

Load-bearing premise

The tested receiver model, sensor box mounting, and local bridge geometry are representative of broader inland waterway deployments.

What would settle it

Repeating the static benchmarking and bridge-passage experiments with a different receiver or at a different waterway site and checking whether RTK still yields consistent precision gains and whether INS still produces measurable drift or jumps upon recovery.

Figures

Figures reproduced from arXiv: 2606.06358 by Jan Swevers, Jef Billet, Peter Slaets, Yan-Yun Zhang.

Figure 1
Figure 1. Figure 1: The Sensor Box with its key GNSS/INS components annotated and sensor frames specified. The design and tuning of such estimators require a realistic understanding of the deployed receiver, which depends on antenna placement, calibration, firmware im￾plementation, operating environment, and receiver sta￾tus. Manufacturer specifications and nominal mode-level accuracy claims therefore do not directly translat… view at source ↗
Figure 2
Figure 2. Figure 2: Recorded path measurements during a canal voyage under [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Analysis of along-track and cross-track position differences ( [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Static benchmarking experimental layout showing three [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Static benchmarking results across four evaluation cases. From left to right, the columns present the measured paths, receiver [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Closed-loop path-following experiments using the INS [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

RTK augmentation andINS integration are widely used to improve GNSS positioning performance. However, on inland waterways, bridges and surrounding structures can degrade satellite visibility and correction availability, causing RTK augmentation loss, and GNSS/INS fusion transients. Since these effects depend on the local environment and sensor configuration, nominal receiver specifications are insufficient, and deployment-specific characterization is required. This paper presents a benchmarking study of an AsteRx-i3 D Pro+ GNSS/INS receiver installed within the mobile Sensor Box developed at KU Leuven. The study combines a real-world bridge-passage case study, static benchmarking, and closed-loop path-following experiments. The static benchmarking evaluates four receiver configurations: standalone GNSS, standalone GNSS with INS integration, RTK-augmented GNSS, and RTK-augmented GNSS with INS integration. The closed-loop experiments use INS-integrated GNSS as the navigation input and compare path-following operational performance with and without RTK augmentation. Results show that correction loss during bridge passage causes reduced positioning accuracy, increased positioning uncertainty and recovery-induced state jumps exceeding 1 m. Static benchmarking and closed-loop experiments confirm that RTK augmentation substantially improves positioning precision and uncertainty consistency, while INS integration supports short-term continuity during RTK unavailability but may introduce drift, bias, or transient uncertainty variations. By characterizing the deployment-specific receiver behavior with RTK augmentation and INS integration, this study motivates higher-level state estimation as a necessary next step toward spatially continuous and uncertainty-consistent positioning on inland waterway. The experimental data are released at: https://doi.org/10.5281/zenodo.20541733.

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

0 major / 2 minor

Summary. This paper presents an empirical benchmarking study of an AsteRx-i3 D Pro+ GNSS/INS receiver in a mobile Sensor Box on inland waterways. It evaluates four configurations (standalone GNSS, GNSS+INS, RTK-augmented GNSS, RTK+INS) via static benchmarking and closed-loop path-following trials, with emphasis on bridge passages that degrade satellite visibility. Results show RTK augmentation improves positioning precision and uncertainty consistency, while INS integration supports short-term continuity during RTK loss but can introduce drift, bias, or transient variations; the study releases the dataset publicly and argues for deployment-specific characterization plus higher-level state estimation.

Significance. If the results hold, the work supplies concrete, deployment-specific measurements of accuracy/continuity trade-offs in a challenging real-world setting (bridge passages on waterways) where nominal receiver specs are shown to be insufficient. The public Zenodo data release (DOI 10.5281/zenodo.20541733) is a clear strength that supports verification and reuse. The study directly ties observed deltas (e.g., >1 m recovery jumps, improved precision with RTK) to the four hardware configurations without post-hoc modeling.

minor comments (2)
  1. [§3] §3 (Experimental Setup): the description of the closed-loop path-following trials could clarify whether all four configurations were used as navigation input or only the INS-integrated ones, to avoid ambiguity about which results apply to which setup.
  2. [Figure 4] Figure 4 or equivalent (bridge-passage time series): axis labels and uncertainty shading could be made more consistent across subplots to improve readability of the transient jumps and recovery behavior.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment, detailed summary of the contributions, and recommendation to accept the manuscript. The report correctly identifies the value of the empirical results on bridge passages, the public Zenodo release, and the motivation for higher-level state estimation. There are no major comments requiring response.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical benchmarking study consisting of static receiver configuration tests and closed-loop path-following trials on specific hardware (AsteRx-i3 D Pro+). No mathematical derivations, fitted parameters, predictions, or first-principles results are present; all reported outcomes are direct measurements from the described experiments and released dataset. The central claims rest on observed deltas in positioning accuracy, uncertainty, and continuity across the four configurations, with no self-referential definitions or load-bearing self-citations that reduce to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model or derivation is present. The work is an empirical comparison of hardware configurations; therefore the ledger contains no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5845 in / 1284 out tokens · 18755 ms · 2026-06-28T00:05:34.757163+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

18 extracted references

  1. [1]

    Report on maritime, inland waterways, fisheries and aquaculture – user needs and requirements,

    European Union Agency for the Space Programme (EUSPA), “Report on maritime, inland waterways, fisheries and aquaculture – user needs and requirements,” Tech. Rep.,

  2. [2]

    Available: https://www.euspa.europa.eu/site s/default/files/report_on_maritime_user_needs_and_req uirements.pdf

    [Online]. Available: https://www.euspa.europa.eu/site s/default/files/report_on_maritime_user_needs_and_req uirements.pdf

  3. [3]

    High definition mapping for inland waterways: Techniques, challenges and prospects,

    L. Hösch, A. Llorente, X. An, J. P. Llerena, and D. Medina, “High definition mapping for inland waterways: Techniques, challenges and prospects,” inProc. IEEE International Con- ference on Intelligent Transportation Systems (ITSC’23), Bil- bao, Spain, 2023, pp. 6034–6041

    2023

  4. [4]

    104,Differential GNSS (Global Navi- gation Satellite Systems) Services, Std

    Radio Technical Commission for Maritime Services (RTCM) Special Committee No. 104,Differential GNSS (Global Navi- gation Satellite Systems) Services, Std. 10403.4, 2024, ver. 3, amend. 1

    2024

  5. [5]

    104, Std

    ——,Networked Transport of RTCM via Internet Protocol (Ntrip), RTCM Special Committee No. 104, Std. 10410.1, 2021, ver. 2, amend. 2

    2021

  6. [6]

    (2026) HxGN SmartNet: GNSS correction services

    Hexagon. (2026) HxGN SmartNet: GNSS correction services. Accessed on 2026-06-03. [Online]. Available: https://hxgnsm artnet.com/

    2026

  7. [7]

    (2022) AGN: Active geodetic network

    National Geographic Institute of Belgium. (2022) AGN: Active geodetic network. Accessed on 2026-06-03. [Online]. Available: https://agn.ngi.be/NL/NL0.jsp

    2022

  8. [8]

    (2017) RTK2GO: Free RTK GNSS base station network

    SubCarrier Systems Corp. (2017) RTK2GO: Free RTK GNSS base station network. Accessed on 2026-06-03. [Online]. Available: http://rtk2go.com/

    2017

  9. [9]

    Directive (EU) 2019/1024 on open data and the reuse of public sector information,

    European Parliament and Council of the European Union, “Directive (EU) 2019/1024 on open data and the reuse of public sector information,” Official Journal of the European Union, 2019. [Online]. Available: http://data.europa.eu/eli/d ir/2019/1024/oj

    2019

  10. [10]

    (2021) AsteRx-i3 D Pro+ GNSS/INS Receiver

    Septentrio. (2021) AsteRx-i3 D Pro+ GNSS/INS Receiver. Accessed on 2026-06-03. [Online]. Available: https://www.se ptentrio.com/en/products/gnss-ins-receivers/gnss-ins-board s/asterx-i3-d-pro-plus

    2021

  11. [11]

    Groves,Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition

    P. Groves,Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition. Boston, MA, USA: Artech House, 2013

    2013

  12. [12]

    Accuracy benchmark of galileo and EGNOS for inland waterways,

    G.Yayla,S.VanBaelen,G.Peeters,M.R.A.Afzal,T.Catoor, Y. Singh, and P. Slaets, “Accuracy benchmark of galileo and EGNOS for inland waterways,” inProc. International Ship Control Systems Symposium (iSCSS’20), Delft, The Nether- lands, 2020

    2020

  13. [13]

    Testing and analysis of selected navigation pa- rameters of the GNSS/INS system for USV path localization during inland hydrographic surveys,

    M. Specht, “Testing and analysis of selected navigation pa- rameters of the GNSS/INS system for USV path localization during inland hydrographic surveys,”Sensors, vol. 24, no. 8, p. 2418, 2024

    2024

  14. [14]

    PerformanceevaluationofGNSSpositionaugmenta- tionmethodsforautonomousvehiclesinurbanenvironments,

    H. B. Swaminathan, A. Sommer, A. Becker, and M. Atz- mueller,“PerformanceevaluationofGNSSpositionaugmenta- tionmethodsforautonomousvehiclesinurbanenvironments,” Sensors, vol. 22, no. 21, p. 8419, 2022

    2022

  15. [15]

    Bar-Shalom, X

    Y. Bar-Shalom, X. R. Li, and T. Kirubarajan,Estimation with applications to tracking and navigation: theory algorithms and software. New York, NY, USA: John Wiley & Sons, 2001

    2001

  16. [16]

    A vessel-agnostic networked sensor suite for autonomy on inland waterways,

    Y.-Y. Zhang, Z. Luo, J. Billet, S. Van Baelen, J. Swevers, H. Bruyninckx, and P. Slaets, “A vessel-agnostic networked sensor suite for autonomy on inland waterways,”Ocean Eng., submitted for publication, preprint available at SSRN: https: //ssrn.com/abstract=6372458

  17. [17]

    Design and build of an autonomous catamaran urban cargo vessel,

    Y.-Y. Zhang, J. Shuai, J. Billet, and P. Slaets, “Design and build of an autonomous catamaran urban cargo vessel,”J. Phys.: Conf. Ser., vol. 2618, p. 012002, 2023

    2023

  18. [18]

    Line-of-sight path-following control utilizing an extended Kalman filter for estimation of speed and course over ground from GNSS positions,

    T. I. Fossen, “Line-of-sight path-following control utilizing an extended Kalman filter for estimation of speed and course over ground from GNSS positions,”J. Mar. Sci. Technol., vol. 27, pp. 806–813, 2022

    2022