SonarLLM: A Native Sonar--Optical Multimodal Large Language Model for Underwater Perception
Reliable underwater perception requires complementary sensing under variable visibility. Optical cameras capture appearance and semantics but degrade rapidly with turbidity, whereas imaging sonar preserves geometry while exhibiting distinct range-azimuth structure and acoustic artifacts. Existing MLLMs, built primarily on optical encoders, are therefore ill-suited to model sonar or adaptively exploit sonar-optical complementarity. We propose SonarLLM, a sonar-optical MLLM that treats sonar as a native perceptual modality. It combines a sonar-specific encoder, modality-specific physics-aware feature enhancement, and reliability-aware hierarchical fusion to align acoustic structure with opt...
| Authors | Cong Su, longxuan ma, Ling Dong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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