基于前视声呐的里程计辅助水下机器人实时建图
Odometry-Aided Real-Time Mapping for Underwater Robots Using Forward-Looking Sonar
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中文总结 AI 辅助
针对水下机器人前视声呐实时建图难题，提出级联特征重建与姿态感知投影方法，实现厘米级精度与低延迟建图。
中文摘要 AI 辅助
可靠感知对于在复杂环境中作业的水下航行器至关重要，因为光衰减和散射常常降低能见度并损害光学传感。前视声呐（FLS）通过在高帧率下提供声学成像，成为光学条件不佳时的替代方案。然而，实时FLS建图仍具挑战性，原因在于目标仰角未解算、空间非均匀噪声以及目标边界破碎，这些因素阻碍特征提取并在投影过程中引入几何模糊性。为应对这些挑战，我们提出一种级联特征重建流程，结合基于快速傅里叶变换（FFT）的去噪、快速多尺度恒虚警率（MCFAR）检测以及梯度自适应边界连接，以低延迟从退化声呐图像中提取几何特征。我们将姿态感知的几何投影与增量占据累积相结合，构建深度参考的2.5D地图，用于受限水下环境中的局部建图。声呐的垂直位置参考外部传感器，而目标仰角在明确的几何假设下赋值，而非由FLS直接测量。在3米×5米水池中的实验展示了厘米级平面建图精度，三条序列的均方根误差（RMSE）低于3厘米，平均处理时间为每帧42.4毫秒。
英文摘要
Reliable perception is essential for underwater vehicles operating in complex environments, where light attenuation and scattering often degrade visibility and compromise optical sensing. Forward-looking sonar (FLS) offers an alternative by providing high-frame-rate acoustic imaging under poor optical conditions. However, real-time FLS mapping remains challenging due to unresolved target elevation, spatially non-uniform noise, and fragmented target boundaries, which hinder feature extraction and introduce geometric ambiguity during projection. To address these challenges, we propose a cascaded feature reconstruction pipeline combining fast Fourier transform (FFT)-based denoising, fast multiscale constant false alarm rate (MCFAR) detection, and gradient-adaptive boundary connection to extract geometric features from degraded sonar images with low latency. We integrate attitude-aware geometric projection with incremental occupancy accumulation to construct a depth-referenced 2.5D map for local mapping in confined underwater environments. The sonar's vertical position is referenced to an external sensor, while target elevation is assigned under an explicit geometric assumption rather than measured directly by FLS. Experiments in a 3 m X 5 m pool demonstrate centimeter-scale planar mapping accuracy, with a root-mean-square error (RMSE) below 3 cm across three sequences and an average processing time of 42.4 ms per frame.
发表机构
- Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
- Tongji University（同济大学）
机构由 AI 辅助整理，请以论文原文为准。
