NeuRSS：利用面向侧扫 SLAM 的神经渲染增强 AUV 定位与水深测图
NeuRSS: Enhancing AUV Localization and Bathymetric Mapping with Neural Rendering for Sidescan SLAM
AI总结：
本文提出 NeuRSS 框架，将神经渲染水深先验融入基于航位推算与长时回环闭合的 SSS SLAM，通过迭代优化联合提升 AUV 定位和水深测图精度。
摘要：
隐式神经表示和神经渲染在利用侧扫声呐（SSS）进行水深估计方面受到越来越多关注。这些方法融合来自 SSS 数据的同一地点的多次观测，以约束高程估计，并收敛到全局一致的水深模型。然而，水深估计的质量和精度受到搭载声呐的自主水下航行器（AUV）定位精度的限制。由于水下缺少 GPS 等地基参考系统，依赖航位推算（DR）的 AUV 全局定位估计会产生无界误差。为应对这一挑战，我们在本信中提出一种现代且可扩展的框架 NeuRSS，用于基于 DR 和大时间尺度回环闭合（LC）的 SSS SLAM，并由利用神经渲染从 SSS 获得的水深估计提供高程先验。该框架是一个迭代过程，可同时改进定位和水深测图。首先，在航位推算估计下由 SSS 估计的水深虽然粗糙，但能在非线性最小二乘（NLS）优化中提供重要的高程先验；该优化用于估计位姿图中两个回环闭合顶点之间的相对位姿。随后，SLAM 组件给出的全局位姿估计改进了航行器的定位估计，从而改进水深估计。我们分别在使用水面船只和 AUV 采集的两个大型测量数据集上验证了定位与测图方法。我们依据真值评估其定位结果，并将水深估计与多波束回声测深仪（MBES）采集的数据进行比较。
英文摘要：
Implicit neural representations and neural rendering have gained increasing attention for bathymetry estimation from sidescan sonar (SSS). These methods incorporate multiple observations of the same place from SSS data to constrain the elevation estimate, converging to a globally-consistent bathymetric model. However, the quality and precision of the bathymetric estimate are limited by the positioning accuracy of the autonomous underwater vehicle (AUV) equipped with the sonar. The global positioning estimate of the AUV relying on dead reckoning (DR) has an unbounded error due to the absence of a geo-reference system like GPS underwater. To address this challenge, we propose in this letter a modern and scalable framework, NeuRSS, for SSS SLAM based on DR and loop closures (LCs) over large timescales, with an elevation prior provided by the bathymetric estimate using neural rendering from SSS. This framework is an iterative procedure that improves localization and bathymetric mapping. Initially, the bathymetry estimated from SSS using the DR estimate, though crude, can provide an important elevation prior in the nonlinear least-squares (NLS) optimization that estimates the relative pose between two loop-closure vertices in a pose graph. Subsequently, the global pose estimate from the SLAM component improves the positioning estimate of the vehicle, thus improving the bathymetry estimation. We validate our localization and mapping approach on two large surveys collected with a surface vessel and an AUV, respectively. We evaluate their localization results against the ground truth and compare the bathymetry estimation against data collected with multibeam echo sounders (MBES).
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