The underwater domain is a challenging environment for robotics because widely used electromagnetic devices must be substituted by acoustic equivalents, much slower and noisier. In this paper a two-dimensional pose simultaneous localization and mapping (SLAM) system for an Autonomous Underwater Vehicle based on inertial sensors and a mechanical profiling sonar is presented. Two main systems are specially designed. On the one hand, a dead reckoning system based on Lie Theory is presented to track integrated pose uncertainty. On the other hand, a rigid scan matching technique specialized for acoustic data is proposed, which allows one to estimate the uncertainty of the matching result. Moreover, Bayesian–Gaussian mixtures models are introduced to the scan matching problem and the registration problem is solved by an optimization in Lie groups. The SLAM system is tested on real data and executed in real time with the robotic application. Using this system, section maps at constant depth can be obtained from a three-dimensional underwater domain. The presented SLAM system constitutes the first achievement towards an underwater Active SLAM application.

中文翻译：

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使用 GMM 扫描匹配进行机械剖面声纳的水下姿态 SLAM

水下领域对于机器人技术来说是一个具有挑战性的环境，因为广泛使用的电磁设备必须被速度更慢、噪音更大的声学设备取代。本文提出了一种基于惯性传感器和机械仿形声纳的自主水下航行器二维姿态同时定位和建图（SLAM）系统。两个主要系统是专门设计的。一方面，提出了一种基于李理论的航位推算系统来跟踪综合姿态不确定性。另一方面，提出了一种专门针对声学数据的刚性扫描匹配技术，该技术允许人们估计匹配结果的不确定性。此外，将贝叶斯-高斯混合模型引入扫描匹配问题，并通过李群优化解决配准问题。SLAM 系统在真实数据上进行测试，并与机器人应用程序一起实时执行。使用该系统，可以从三维水下域获得恒定深度的剖面图。所提出的 SLAM 系统构成了水下主动 SLAM 应用的第一个成就。