Implicit neural representations and neural render- ing 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).

翻译：隐式神经表示与神经渲染技术在侧扫声纳(SSS)水深估算中日益受到关注。这些方法通过融合SSS数据中同一地点的多次观测值约束高程估算，最终收敛至全局一致的水深模型。然而，水深估算的质量与精度受限于搭载声纳的自主水下航行器(AUV)的定位精度。由于水下缺乏GPS等地理参考系统，依赖航位推算(DR)的AUV全局定位估计存在无界误差。为解决这一挑战，本文提出一种现代可扩展框架——NeuRSS，该框架基于DR与大时间尺度闭环(LCs)实现SSS SLAM，并通过SSS神经渲染提供的高程先验辅助水深估算。该框架通过迭代过程同时优化定位与水深制图：初始阶段，利用DR估计从SSS估算的粗糙水深虽不精确，但可为非线性最小二乘(NLS)优化提供重要高程先验——该优化用于估计位姿图中两个闭环节点间的相对位姿；随后，SLAM模块提供的全局位姿估计改善载体定位精度，进而提升水深估算质量。我们分别基于水面船与AUV采集的两组大规模调查数据验证了定位与制图方法，并将定位结果与真值对比，同时将水深估算与多波束回声测深仪(MBES)采集的数据进行比对。