Odometry-Aided Real-Time Mapping for Underwater Robots Using Forward-Looking Sonar

2026-09-21

AI Summary

为解决水下机器人在复杂环境中的感知问题，提出了一种结合FFT去噪、MCFAR检测和梯度自适应边界连接的方法，利用前视声呐实现实时建图。

Abstract

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.

Problem

• Forward-looking sonar
• Real-time mapping
• Underwater robots
• Optical sensing degradation
• Geometric ambiguity

Innovation

• cascaded feature reconstruction pipeline
• fast Fourier transform (FFT)-based denoising
• multiscale constant false alarm rate (MCFAR) detection
• gradient-adaptive boundary connection
• attitude-aware geometric projection