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The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization

Derek R. Benham, Joshua G. Mangelson

arXiv:2608.21276v1cs.ROcs.CV

TL;DR

GPS-denied ASV localization is difficult because conventional systems drift without global position and coastal scenes offer sparse traditional structure. The paper uses shoreline and water-surface geometry in complementary LiDAR and monocular pipelines, finding improved LiDAR trajectories and bounded visual drift across three coastal datasets.

  • Problem

    GPS-dependent systems drift without regular global position, while conventional SLAM lacks an external global reference for maritime operation.

    Source: §I · p. 1

  • Method

    The paper combines LiDAR water-surface estimation and shoreline-to-satellite registration with monocular semantic shoreline and horizon extraction, submaps, and hierarchical factor-graph fusion.

    Source: §I · p. 2, 1) Semantic Segmentation and Boundary… · pp. 9–10

  • Results

    Across three real-world coastal datasets, Coastal-KISS consistently improved trajectory accuracy while the visual framework achieved globally bounded localization through shoreline-to-satellite registration.

    Source: §VI · p. 20

  • Takeaways & Limitations

    Coastal geometry provides a powerful source of globally referenced information for GPS-denied maritime localization, and zero-shot segmentation supports shoreline extraction across diverse environments.

    Source: §V · p. 18, §VI · p. 20

    The vision frontend can fail around vessels, and tidal fluctuations may degrade alignment between observed shorelines and satellite maps.

    Source: D. Limitations of Segmentation and… · p. 19

Abstract

from arXiv · show

Coastal environments contain rich, largely unexploited geometric structure capable of providing globally referenced localization cues. In this work, we present two complementary localization frameworks that exploit shoreline and water-surface geometry for GPS-denied autonomous surface vessel localization. The first framework leverages LiDAR observations of the water surface to estimate roll, pitch, and heave (vertical motion), while recovering global position and heading through direct registration of shoreline observations against a satellite-derived coastline map. The second framework relies solely on passive imagery to detect the shoreline and horizon through semantic segmentation. Using the proposed coastal scene geometry, shoreline distance is inferred from monocular imagery. Shoreline observations are accumulated into short-duration local submaps, registered against the same satellite-derived coastline map, and fused within a hierarchical factor graph. Evaluated across three real-world coastal datasets, the LiDAR pipeline consistently improves trajectory accuracy over standard baselines, while the monocular architecture maintains bounded long-term drift. In addition, we establish that modern zero-shot foundation models can reliably extract shoreline observations across diverse coastal environments. Together, these results demonstrate that coastal geometry provides a powerful and dependable source of globally referenced information for GPS-denied maritime localization.

I. INTRODUCTION

Autonomous surface vessels need robust localization because GPS-dependent systems drift during outages, while conventional mapping and SLAM approaches face global-reference and computational limitations. This paper exploits persistent coastal geometry through complementary LiDAR and monocular pipelines, evaluated across real-world coastal datasets.

  • GPS-dependent ASV systems ultimately drift and fail without regular global position estimates.Source: §I · p. 1
  • Prior-map localization restricts operation to mapped regions and depends on reference-map fidelity and recency.Source: §I · p. 1
  • Traditional SLAM provides only a local frame without external global references, while long missions can make graph optimization computationally prohibitive.Source: §I · pp. 1–2
  • Coastal scenes provide a reference plane, horizon, and persistent shoreline observable from vehicle sensors and satellite imagery.Source: §I · p. 2
  • The proposed LiDAR and monocular pipelines exploit water-surface and shoreline geometry for globally referenced localization.Source: §I · p. 2
  • The work evaluates the unified framework across three real-world coastal datasets totaling nearly 5 km of operation.Source: §I · p. 2

II. RELATED WORK

Related work shows that maritime localization methods often trade global reference, robustness, and environmental coverage against sensing and computational constraints. Existing LiDAR, radar, and vision systems motivate specialized approaches that exploit coastal structure directly.

  • Early ASV state-estimation methods relied on GPS or operated in sheltered waters where wave-induced motion was negligible.Source: §II · p. 3

Radar

  • Radar is robust to water clutter and atmospheric obscurants but provides coarser geometry because of lower angular resolution and multipath interference.Source: 1) Radar · p. 3

LiDAR

  • LiDAR offers denser, more precise observations than radar but has reduced range and greater sensitivity to water-surface reflections and backscatter.Source: 1) Radar · p. 3
  • Existing LiDAR maritime methods improved odometry robustness but remained fundamentally dependent on GNSS for global position.Source: 1) Radar · p. 3

Cameras

  • Vision-based odometry and SLAM methods remain constrained by shoreline proximity or fail to provide globally referenced coordinates in open-water settings.Source: 3) Cameras · p. 4

B. Cross-View Localization

Cross-view localization aligns vehicle observations with geo-referenced overhead maps to bound drift, but prior methods often lacked temporal consistency, broad modality coverage, or full coastal scene structure. Semantic segmentation and zero-shot foundation models provide a route to extracting the coastal boundaries needed by the proposed framework.

  • Cross-view localization recovers global position by registering local maps against geo-referenced overhead representations such as satellite imagery.Source: B. Cross-View Localization · p. 4
  • Independent radar-scan alignment can outperform dead reckoning but limits smooth continuous trajectories between observations.Source: B. Cross-View Localization · p. 4
  • Prior satellite-based methods demonstrated bounded drift but remained limited to specific sensing modalities and underused coastal geometric and semantic structure.Source: B. Cross-View Localization · p. 4
  • Semantic segmentation can support state estimation using high-level scene understanding while requiring only broad coastal classes for this framework.Source: C. Semantic Coastal Scene Understanding · p. 4

Semantic Coastal Scene Understanding

III. METHODS

The methods derive two localization pipelines from a shared coastal scene model in which the mean water surface and coastline provide stationary geometric references. LiDAR and monocular sensing use these constraints through complementary pose and shoreline-estimation strategies.

  • The paper proposes LiDAR and vision methods that derive drift-free geometric constraints from the shoreline and water surface.Source: §III · p. 6
  • Both pipelines use a common world and vehicle frame model with the mean water surface represented as a planar reference.Source: A. Coastal Scene Modeling and… · p. 6
  • The model assumes time-averaged water-surface elevation remains constant during a mission despite instantaneous wave perturbations.Source: A. Coastal Scene Modeling and… · p. 6
  • The coastline is modeled as the intersection of terrain and mean water surface, with tidal variation assumed negligible over 15–20-minute missions.Source: A. Coastal Scene Modeling and… · p. 6

B. LiDAR based Localization

The LiDAR pipeline uses the water surface to estimate vertical and attitude states, then registers shoreline geometry against a satellite-derived coastline map to recover planar pose and globally referenced localization.

LiDAR-Based Ground Plane Estimation

LiDAR Cross-View Satellite Image Alignment

  • Rectified shoreline points are orthographically projected into a 2D occupancy grid that approximates a satellite view for cross-view registration.The projection reduces the vertical coordinate after correcting the point cloud using estimated roll and pitch.Source: B. LiDAR based Localization · p. 6, 2) LiDAR · pp. 7–8

LiDAR-Based Ground Plane Estimation

  • The LiDAR range image is processed structurally to isolate first-return boundaries associated with the shoreline, wake, or wave crests.Morphological closing suppresses isolated returns before a bottom-up column search extracts the boundary signal.Source: 1) LiDAR-Based Ground Plane Estimation… · p. 7

LiDAR Cross-View Satellite Image Alignment

  • Localized ICP aligns the simulated satellite view with a prior coastline map while restricting optimization to planar position (x, y) and heading (ψ).Initialization from the current pose estimate limits the search window and helps prevent convergence to local minima.Source: 2) LiDAR · p. 8

Factor Graph SLAM with LiDAR Odometry

  • A fixed-lag factor graph fuses KISS-ICP odometry with water-surface and shoreline observations to produce globally referenced pose estimates.Water-plane measurements constrain roll, pitch, and height, while shoreline registration constrains planar position and yaw.Source: 3) Factor Graph SLAM with LiDAR Odometry · p. 8, Figure 5 · p. 8

C. Vision-Based Localization

The vision pipeline extracts horizon and shoreline geometry from monocular images, reconstructs shoreline observations, and registers short-duration submaps against satellite coastline maps. A hierarchical factor graph combines local high-rate estimation with sparse global optimization, while shoreline geometry remains less observable along straight coastlines.

IV. RESULTS AND INSIGHTS

The evaluation spans three real-world coastal datasets and assesses both localization frameworks, including the visual perception front-end and a LiDAR odometry ablation. It also examines why conventional LiDAR odometry struggles in coastal environments.

  • Three real-world coastal datasets are used to evaluate the LiDAR and monocular localization pipelines independently.Source: §IV · p. 11
  • The vision evaluation additionally examines the perception front-end and includes an ablation study of conventional LiDAR odometry difficulties in coastal environments.Source: §IV · p. 11

A. System and Dataset Overview

The study evaluates coastal localization using a sensorized autonomous surface vessel across diverse O‘ahu datasets, with RTK-GPS providing quantitative reference measurements. Results show that standard LiDAR odometry often struggles in open water, while coastal geometric constraints improve trajectory estimation.

A. System and Dataset Overview

B. LiDAR Results

C. Semantic Segmentation Evaluation

The evaluation compares semantic segmentation architectures for extracting shoreline boundaries from coastal imagery. Zero-shot foundation-model pipelines generally provide more reliable boundaries, while Grounded-MobileSAM offers the strongest accuracy-efficiency trade-off despite prompting overhead.

Evaluation setup

  • The evaluation uses region-based IoU, per-class IoU, 95th-percentile shoreline error, and failure rate to assess segmentation and shoreline extraction.Shoreline-specific error is emphasized because registration depends on water-land boundary alignment.Source: C. Semantic Segmentation Evaluation · p. 14

Generalization

  • YOLO11-Seg performs well within its training environment but fails to generalize to unseen shorelines and environmental conditions.The model was fine-tuned on 350 labeled images from a small subsection of one dataset.Source: C. Semantic Segmentation Evaluation · p. 14

Model comparison

Model trade-offs

  • SAM 3 produces coherent segmentations across varied scenes but has less precise shoreline boundaries and higher computational cost than stronger boundary-focused alternatives.Its boundary imprecision increases localization error despite competitive mean IoU scores.Source: C. Semantic Segmentation Evaluation · p. 16

Quantitative results

  • Grounded-MobileSAM achieves the highest overall segmentation accuracy, with the highest mean IoU and lowest shoreline segmentation error.Its reliance on Grounding DINO prompts for every frame limits processing speed.Source: C. Semantic Segmentation Evaluation · p. 16

D. Vision-Based Localization Results

The proposed vision localization framework is evaluated against local, global, visual-inertial, and LiDAR odometry baselines across three coastal datasets. Shoreline submap alignment reduces translational error, while hierarchical global optimization extends corrections beyond the local estimator’s retained history.

Baseline comparison

  • Conventional visual baselines fail or drift substantially when nearby land features are limited, including 827 m translational error for VINS-Mono.Neither VINS-Mono nor ORB-SLAM3 successfully processes Makali‘i Point; ORB-SLAM3 also fails in K¯ane‘ohe Bay.Source: D. Vision-Based Localization Results · p. 17

Vision-based localization results

  • Shoreline submap alignment substantially reduces translational error relative to the local odometry baseline across all three datasets.Adding the hierarchical global graph yields further reductions, particularly in K¯ane‘ohe Bay and ‘Anahulu River.Source: D. Vision-Based Localization Results · p. 17

Hierarchical optimization

LiDAR ablation

  • Restricting LiDAR returns to land improves odometry in the ‘Anahulu River dataset when calm water and abundant land returns are available.The result suggests that removing water-surface reflection returns can improve trajectory estimation under those conditions.Source: D. Vision-Based Localization Results · p. 18
  • Open-water datasets show little benefit from land-only LiDAR masking because shoreline returns are sparse, distant, and available from limited viewing directions.The loss of valid land measurements can outweigh gains from suppressing water-surface artifacts.Source: D. Vision-Based Localization Results · p. 18

V. DISCUSSION AND LESSONS LEARNED

The experiments show that exploiting coastal scene geometry enables globally consistent localization in GPS-denied maritime environments.

Discussion

  • Exploiting coastal scene geometry enables globally consistent localization in GPS-denied maritime environments.The discussion frames this result alongside sensing, environmental, and architectural trade-offs observed during field evaluation.Source: §V · p. 18

A. Maritime Environments Are Not Homogeneous

Coastal environments differ substantially in sensing conditions and geometric observability, producing distinct perception and localization failure modes. These differences create a trade-off between LiDAR’s spatial fidelity and vision’s attitude constraints.

Maritime Environments Are Not Homogeneous

  • Calm rivers, sheltered bays, and exposed coastlines provide substantially different quantities and qualities of geometric observations.The three datasets were collected in one geographic region but still exhibited distinct sensing conditions and failure modes.Source: A. Maritime Environments Are Not… · p. 18
  • Specular water reflections can corrupt LiDAR water-plane estimates and create false shoreline boundaries in the visual pipeline.The resulting errors affect roll, pitch, height, and estimated land distance.Source: A. Maritime Environments Are Not… · p. 19
  • Conventional LiDAR odometry performs well in the river because dense bank vegetation provides features on both sides, but routinely fails in open water.Open-water scenes offer sparse, distant land returns often available on only one side of the vessel.Source: A. Maritime Environments Are Not… · p. 19
  • LiDAR directly measures range for high-fidelity shoreline reconstruction and registration, whereas vision estimates shoreline distance from segmentation, calibration, and attitude.The visual pipeline is therefore more sensitive to segmentation and attitude-estimation errors.Source: B. Complementary Strengths of LiDAR… · p. 19
  • Vision provides informative horizon-based attitude constraints, while LiDAR provides superior spatial fidelity and consistently lower trajectory error.The visual framework depends more on accumulated observations and delayed global corrections.Source: B. Complementary Strengths of LiDAR… · p. 19

C. Observability Degradation during Prolonged

The visual localization framework remains vulnerable when coastal geometry provides weak motion observability or when segmentation and map assumptions fail. These limitations emphasize that maritime localization depends on environment-specific observability and assumptions.

Straightaways

  • Localization error grows most rapidly along locally linear coastlines because shoreline registration provides little information about motion parallel to the coast.Position error can accumulate in that direction during extended passes.Source: C. Observability Degradation during… · p. 19
  • Hierarchical smoothing retroactively distributes corrections, but pronounced error accumulation persists during prolonged straightaways when dead reckoning is weak.The paper identifies affected trajectory segments in Makali´i Point and K¯ane´ohe Bay.Source: C. Observability Degradation during… · p. 19

Segmentation and map dependencies

  • The visual frontend assumes every pixel not classified as water or sky is land, an assumption that breaks down around dynamic or stationary vessels.This introduces vulnerability through the segmentation frontend.Source: D. Limitations of Segmentation and… · p. 19

Broader implications

  • The results show that maritime localization depends on sensing conditions, observability characteristics, and environmental assumptions rather than sensing capability alone.Different coastal settings introduce distinct sources of uncertainty and failure.Source: D. Limitations of Segmentation and… · p. 19, §VI · p. 20

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