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

arXiv: https://arxiv.org/abs/2608.21276 · HTML: https://mlsanity.com/paper/2608.21276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 81 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 30 of 33)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [Fast image-based obstacle detection from unmanned surface vehicles](https://mlsanity.com/paper/1503.01918/) (2015)
- [Are We Ready for Unmanned Surface Vehicles in Inland Waterways? The USVInland Multisensor Dataset and Benchmark](https://mlsanity.com/paper/2103.05383/) (2021)
- [Satellite Image Based Cross-view Localization for Autonomous Vehicle](https://mlsanity.com/paper/2207.13506/) (2022)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation](https://mlsanity.com/paper/2405.15664/) (2024)
- [Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?](https://mlsanity.com/paper/2203.10174/) (2022)
- [eWaSR -- an embedded-compute-ready maritime obstacle detection network](https://mlsanity.com/paper/2304.11249/) (2023)
- [Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar](https://mlsanity.com/paper/2311.08608/) (2023)
- [Memory Maps for Video Object Detection and Tracking on UAVs](https://mlsanity.com/paper/2303.03508/) (2023)
- [Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images](https://mlsanity.com/paper/2309.14657/) (2023)
- [GenZ-ICP: Generalizable and Degeneracy-Robust LiDAR Odometry Using an Adaptive Weighting](https://mlsanity.com/paper/2411.06766/) (2024)
- [MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application](https://mlsanity.com/paper/2412.03887/) (2024)
- [Monocular Visual Odometry for an Unmanned Sea-Surface Vehicle](https://mlsanity.com/paper/1707.04444/) (2017)
- [RaSCL: Radar to Satellite Crossview Localization](https://mlsanity.com/paper/2504.15899/) (2025)
- [Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation](https://mlsanity.com/paper/2505.01615/) (2025)

## Abstract (arXiv)

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.