ReefMapGS: Enabling Large-Scale Underwater Reconstruction by Closing the Loop Between Multimodal SLAM and Gaussian Splatting

2026-04-13

AI Summary

This work addresses the challenge of high computational cost in pose estimation and the difficulty of deploying underwater large-scale 3D reconstruction on field robots. The authors propose ReefMapGS, a novel framework that, for the first time, integrates multimodal SLAM with 3D Gaussian splatting in a closed-loop manner, enabling incremental reconstruction without relying on COLMAP. The method leverages acoustic, inertial, pressure, and visual sensors to construct a pose-graph SLAM system, initializes Gaussian primitives in high-confidence regions, and alternates between local image-based tracking and global Gaussian optimization. Evaluated on two complex coral reef environments, the system achieves COLMAP-free reconstruction over 700-meter AUV trajectories, significantly improving both global pose accuracy and reconstruction efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

3D Gaussian Splatting is a powerful visual representation, providing high-quality and efficient 3D scene reconstruction, but it is crucially dependent on accurate camera poses typically obtained from computationally intensive processes like structure-from-motion that are unsuitable for field robot applications. However, in these domains, multimodal sensor data from acoustic, inertial, pressure, and visual sensors are available and suitable for pose-graph optimization-based SLAM methods that can estimate the vehicle's trajectory and thus our needed camera poses while providing uncertainty. We propose a 3DGS-based incremental reconstruction framework, ReefMapGS, that builds an initial model from a high certainty region and progressively expands to incorporate the whole scene. We reconstruct the scene incrementally by interleaving local tracking of new image observations with optimization of the underlying 3DGS scene. These refined poses are integrated back into the pose-graph to globally optimize the whole trajectory. We show COLMAP-free 3D reconstruction of two underwater reef sites with complex geometry as well as more accurate global pose estimation of our AUV over survey trajectories spanning up to 700 m.

Problem

• 3D Gaussian Splatting
• underwater reconstruction
• camera pose estimation
• SLAM
• field robotics

Innovation

• 3D Gaussian Splatting
• multimodal SLAM
• incremental reconstruction
• pose-graph optimization
• underwater 3D reconstruction