Seabed-to-Sky Mapping of Maritime Environments with a Dual Orthogonal SONAR and LiDAR Sensor Suite
- URL: http://arxiv.org/abs/2512.05303v1
- Date: Thu, 04 Dec 2025 22:53:32 GMT
- Title: Seabed-to-Sky Mapping of Maritime Environments with a Dual Orthogonal SONAR and LiDAR Sensor Suite
- Abstract summary: Critical maritime infrastructure increasingly demands situational awareness both above and below the surface.<n>We present a unified, independent mapping system that fuses LiDAR-IMU with a dual, orthogonally mounted Forward Looking Sonars.<n>On the acoustic side, we extend wide-aperture fusion to handle arbitrary inter-sonar translations and extract a leading edge from each FLS to form line-scans.<n>We validate the system on real-world data from Belvederekanalen (Copenhagen) demonstrating real-time operation with approx. 2.65 Hz Hz updates and approx. 2.85 Hz odometry.
- Score: 0.3694429692322631
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Critical maritime infrastructure increasingly demands situational awareness both above and below the surface, yet existing ''seabed-to-sky'' mapping pipelines either rely on GNSS (vulnerable to shadowing/spoofing) or expensive bathymetric sonars. We present a unified, GNSS-independent mapping system that fuses LiDAR-IMU with a dual, orthogonally mounted Forward Looking Sonars (FLS) to generate consistent seabed-to-sky maps from an Autonomous Surface Vehicle. On the acoustic side, we extend orthogonal wide-aperture fusion to handle arbitrary inter-sonar translations (enabling heterogeneous, non-co-located models) and extract a leading edge from each FLS to form line-scans. On the mapping side, we modify LIO-SAM to ingest both stereo-derived 3D sonar points and leading-edge line-scans at and between keyframes via motion-interpolated poses, allowing sparse acoustic updates to contribute continuously to a single factor-graph map. We validate the system on real-world data from Belvederekanalen (Copenhagen), demonstrating real-time operation with approx. 2.65 Hz map updates and approx. 2.85 Hz odometry while producing a unified 3D model that spans air-water domains.
Related papers
- Geometry-Driven Opti-Acoustic Co-Registration and View-Invariant Reflectivity Mapping for Side-Scan Sonar [0.0]
Side-Scan Sonar (SSS) is a primary modality for large-scale underwater mapping.<n>Traditional descriptors fail to bridge the physical domain gap between optical and acoustic imagery.<n>We propose a novel framework for pixel-level opti-acoustic co-registration and view-invariant reflectivity mapping.
arXiv Detail & Related papers (2026-08-24T16:44:46Z) - Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
arXiv Detail & Related papers (2026-08-12T11:25:31Z) - Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups [8.422269543220251]
We propose a Geometry-Aware Hybrid Fusion (GA-HF) framework that accounts for fisheye geometry and BEV feature distortion.<n>GA-HF is evaluated on three benchmarks: KITTI-360, Dur360BEV, and Fisheye3DOD datasets.
arXiv Detail & Related papers (2026-06-07T21:26:00Z) - LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting [8.537296437078387]
We present LiftNav, a hybrid navigation framework built on GSFusion's TSDF+GS dual map.<n>This design enables flexible semantic navigation without dense 3D embeddings.<n>Compared against a state-of-the-art radiance field baseline we show a 100% feasibility rate and shorter trajectories.
arXiv Detail & Related papers (2026-05-29T14:44:59Z) - OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation [100.36917520403227]
We present a unified text-conditioned diffusion framework that generates LiDAR scans in a shared range-image representation across eight representative domains.<n>In the absence of a public consolidated benchmark, we construct an 8-domain dataset by combining real-world scans with physically based weather simulation and systematic beam reduction.
arXiv Detail & Related papers (2026-05-13T17:42:20Z) - VLMFusionOcc3D: VLM Assisted Multi-Modal 3D Semantic Occupancy Prediction [0.0]
VLMFusionOcc3D is a robust multimodal framework for dense 3D semantic occupancy prediction in autonomous driving.<n>We introduce Weather-Aware Adaptive Fusion, a dynamic gating mechanism that utilizes vehicle metadata and weather-conditioned prompts to re-weight sensor contributions.<n>Our approach achieves significant improvements in challenging weather scenarios, offering a scalable and robust solution for complex urban navigation.
arXiv Detail & Related papers (2026-03-03T05:22:28Z) - StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation [18.410248448681514]
We propose StereoAdapter-2, which replaces the conventional ConvGRU updater with a novel ConvSS2D operator.<n>We construct UW-StereoDepth-80K, a large-scale synthetic underwater stereo dataset.<n>Our framework achieves state-of-the-art zero-shot performance on underwater benchmarks.
arXiv Detail & Related papers (2026-02-18T22:12:08Z) - Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing [59.12032628787018]
3D mesh saliency ground truth is essential for human-centric visual modeling in virtual reality (VR)<n>Current VR eye-tracking pipelines rely on single ray sampling and Euclidean smoothing, triggering texture attention and signal leakage across gaps.<n>This paper proposes a robust framework to address these limitations.
arXiv Detail & Related papers (2026-01-06T05:20:12Z) - FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry [28.606325312582218]
We propose FAST-LIVO2, a fast, direct LiDAR-inertial-visual odometry framework to achieve accurate and robust state estimation in SLAM tasks.
FAST-LIVO2 fuses the IMU, LiDAR and image measurements efficiently through a sequential update strategy.
We show three applications of FAST-LIVO2, including real-time onboard navigation, airborne mapping, and 3D model rendering.
arXiv Detail & Related papers (2024-08-26T06:01:54Z) - DSGN++: Exploiting Visual-Spatial Relation forStereo-based 3D Detectors [60.88824519770208]
Camera-based 3D object detectors are welcome due to their wider deployment and lower price than LiDAR sensors.
We revisit the prior stereo modeling DSGN about the stereo volume constructions for representing both 3D geometry and semantics.
We propose our approach, DSGN++, aiming for improving information flow throughout the 2D-to-3D pipeline.
arXiv Detail & Related papers (2022-04-06T18:43:54Z) - TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view
Stereo [55.30992853477754]
We present TANDEM, a real-time monocular tracking and dense framework.
For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of alignments.
TANDEM shows state-of-the-art real-time 3D reconstruction performance.
arXiv Detail & Related papers (2021-11-14T19:01:02Z) - Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth
Estimation [81.08111209632501]
We propose a geometry-aware stereo-LiDAR fusion network for long-range depth estimation.
We exploit sparse and accurate point clouds as a cue for guiding correspondences of stereo images in a unified 3D volume space.
Our network achieves state-of-the-art performance on the KITTI and the Virtual- KITTI datasets.
arXiv Detail & Related papers (2021-03-24T03:24:46Z) - End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection [62.34374949726333]
Pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras.
PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs.
We introduce a new framework based on differentiable Change of Representation (CoR) modules that allow the entire PL pipeline to be trained end-to-end.
arXiv Detail & Related papers (2020-04-07T02:18:38Z) - OmniSLAM: Omnidirectional Localization and Dense Mapping for
Wide-baseline Multi-camera Systems [88.41004332322788]
We present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras.
For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation.
We integrate our omnidirectional depth estimates into the visual odometry (VO) and add a loop closing module for global consistency.
arXiv Detail & Related papers (2020-03-18T05:52:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.