AI 中文总结
本文提出AquaBEV-Nav，一种基于直接鸟瞰图占用预测的水下探索框架，绕过单目深度估计，在模拟礁石环境中实现高IoU和零碰撞的闭环覆盖。
AI 中文摘要
安全的水下探索要求机器人了解周围结构的位置以及哪些区域可供运动。现有的基于视觉的水下探索系统通常通过估计单目深度、将几何体反投影到3D空间并将其累积到2D鸟瞰图占用地图中来间接获取此信息。这种对中间深度估计的依赖在水下尤其成问题，因为散射和波长相关的衰减会削弱视觉线索，限制单目深度估计的可靠性。我们引入了AquaBEV-Nav，一种水下探索框架，通过直接进行鸟瞰图占用预测来绕过显式的单目深度估计。基于CORAL分层探索框架，AquaBEV-Nav用AquaBEV替换了其基于深度的感知前端。给定单个RGB帧，AquaBEV将视觉特征映射到习得的极坐标表示中，沿距离维度进行因果推理，并在不依赖中间深度预测的情况下重建局部笛卡尔占用。生成的占用图被累积到CORAL的持久空间记忆中，为基于VLM的高级规划提供空间上下文，并为动态感知的局部轨迹生成提供碰撞约束。在十个模拟礁石环境和六个占用骨干网络下，在单一协议下进行评估，AquaBEV-Nav达到了37.48的结构IoU和53.2的目标IoU，88.95%的闭环覆盖率，且零碰撞。
英文摘要
Safe underwater exploration requires a robot to understand where surrounding structures are located and which regions are available for motion. Existing vision-based underwater exploration systems commonly obtain this information indirectly by estimating monocular depth, unprojecting the geometry into 3D space, and accumulating it into a 2D bird's-eye-view occupancy map. This reliance on intermediate depth estimation is particularly problematic underwater, where scattering and wavelength-dependent attenuation degrade visual cues and limit the reliability of monocular depth estimates. We introduce AquaBEV-Nav, an underwater exploration framework that bypasses explicit monocular depth estimation through direct bird's-eye-view occupancy prediction. Built upon the CORAL hierarchical exploration framework, AquaBEV-Nav replaces its depth-based perception front end with AquaBEV. Given a single RGB frame, AquaBEV maps visual features into a learned polar representation, performs causal reasoning along the range dimension, and reconstructs local Cartesian occupancy without relying on intermediate depth prediction. The resulting occupancy map is accumulated into CORAL's persistent spatial memory, providing spatial context for VLM-based high-level planning and collision constraints for dynamics-aware local trajectory generation. Across ten simulated reef environments and six occupancy backbones evaluated under a single protocol, AquaBEV-Nav reaches 37.48 structure IoU and 53.2 target IoU, 88.95% closed-loop coverage with zero collisions.
