APVI-SLAM: Real-Time Acoustic-Pressure-Visual-Inertial Localization and Photorealistic Mapping System in Complex Underwater Environment

• URL: http://arxiv.org/abs/2607.06222v1
• Date: Tue, 07 Jul 2026 12:45:57 GMT
• Title: APVI-SLAM: Real-Time Acoustic-Pressure-Visual-Inertial Localization and Photorealistic Mapping System in Complex Underwater Environment
• Abstract summary: APVI-SLAM is a real-time multi-sensor fusion SLAM system that achieves both accurate underwater localization and photorealistic mapping.<n>Our approach introduces a reliability-aware localization framework that dynamically reweights sensor estimators.<n>For high-fidelity scenes reconstruction, we propose an efficient quadtree-guided mapping module.
• Score: 22.489669412924346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extreme subsea environments often cause severe feature de-gradation and estimator divergence in underwater visual-inertial SLAM. Although sensors like Doppler Velocity Logs (DVL) and pressure gauges provide auxiliary constraints, robust multi-sensor fusion during intermittent visual failure remains challenging. To address this, we present APVI-SLAM, a real-time multi-sensor fusion SLAM system that achieves both accurate underwater localization and photorealistic mapping. Our approach introduces a reliability-aware localization framework that dynamically reweights sensor estimators and employs a sliding-window freezing strategy to recover from tracking failures, substantially enhancing system robustness. Furthermore, for high-fidelity scenes reconstruction, we propose an efficient quadtree-guided mapping module that facilitates incremental water-medium modeling and 3D Gaussian optimization. Recognizing the lack of benchmark for underwater mapping evaluation, we also contribute a coral reef surveying dataset with synchronized multi-modality data. Extensive experiments on public and our proposed benchmarks demonstrate that APVI-SLAM achieves state-of-the-art localization and reconstruction quality at real-time speeds.

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