发表机构
Texas A&M University（德克萨斯A&M大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对仅前视声呐的局部SLAM空白，提出基于逆合成高斯-牛顿的密集直接配准方法，实现实时定位，显著降低平移误差，性能媲美多传感器融合，并在嵌入式平台验证。
AI 中文摘要
自主水下导航通常依赖于复杂且昂贵的多模态传感器套件，这些套件旨在优先保证全局同步定位与建图（SLAM）的精度。然而，诸如粗略导航和避障等局部反应性行为仅需要局部一致性——这一能力应仅使用前视声呐（FLS）即可实现，但至今仍未得到充分解决，在仅FLS的局部SLAM中留下了关键空白。此外，现有的声学SLAM框架主要依赖稀疏特征提取方法，这些方法丢弃了本已信息稀疏的声学回波中的大量信息。为克服这些局限，本工作引入了一种密集直接配准方法，将完整的声学强度扫描与递归更新的局部地图对齐。通过逆合成高斯-牛顿优化策略实现实时执行，该策略最小化计算开销。实验评估表明，与稀疏关键点基线相比，这种密集方法在平移误差上带来了显著改进，在较大的位移间隔内保持稳定的亚米级跟踪精度。此外，该方法在特征丰富的环境中提供了与多传感器融合流水线（FLS、DVL和IMU）相当的里程计性能，绕过了昂贵的载荷依赖。我们通过AUV现场试验验证了实际适用性，在嵌入式、资源受限的计算机上机载运行完整的局部SLAM方法。
英文摘要
Autonomous underwater navigation typically relies on complex and expensive multi-modal sensor suites designed to prioritize global Simultaneous Localization and Mapping (SLAM) accuracy. However, local reactive behaviors such as coarse navigation and obstacle avoidance require only local consistency---a capability that should be feasible using only a Forward-Looking Sonar (FLS), yet remains largely unaddressed, leaving a critical gap in FLS-only local SLAM. Moreover, existing acoustic SLAM frameworks predominantly rely on sparse feature extraction methods that discard substantial portions of the already information-sparse acoustic returns. To overcome these limitations, this work introduces a dense direct registration approach that aligns full acoustic intensity scans to a recursively updated local map. Real-time execution is achieved via an Inverse Compositional Gauss-Newton optimization strategy that minimizes computational overhead. Experimental evaluations show that this dense method yields significant improvements on translation error compared to sparse keypoint baselines, maintaining stable sub-meter tracking precision over wide displacement gaps. Moreover, this approach delivers odometry performance comparable to multi-sensor fusion pipelines (FLS, DVL, and IMU), bypassing expensive payload dependencies in feature-rich environments. We validate real-world applicability through AUV field trials, running the full local SLAM approach onboard an embedded, resource-constrained computer.
Comments8 pages, 5 figures, preprint
