ALAR: A Multimodal Underwater Dataset for Harsh-Domain Perception

ALAR dataset is a synthetic multimodal underwater perception dataset designed to support research on imaging sonar, underwater vision, multimodal learning, and sim-to-real transfer in harsh sensing conditions. The dataset was generated with ALAR, a runtime-configurable extension of HoloOcean that enables reproducible scene population with custom underwater assets and explicit sonar refresh after scene updates, ensuring that runtime-inserted geometry remains acoustically observable. Data were acquired with a simulated BlueROV2 platform equipped with a front RGB camera, a bottom RGB camera, an imaging sonar, and auxiliary telemetry.