MCHS-SLAM: A Multi-Constraint Hybrid Strategy SLAM Framework for AUV-Based Seafloor Terrain Mapping

During seafloor terrain mapping missions conducted by AUVs, positioning error accumulation occurs inevitably over long distances due to the unavailability of global satellite navigation signals underwater. Moreover, the alternating distribution of flat and undulating regions on the seafloor renders single-constraint-based bathymetric SLAM methods prone to performance degradation in complex environments. To address these challenges, this paper proposes a multi-constraint hybrid strategy SLAM framework for AUV-based seafloor terrain mapping, grounded in an analysis of error accumulation mechanisms and constraint failure characteristics. The framework establishes a hierarchical and progressive constraint architecture to enable collaborative optimization across different spatial scales and topographic conditions. At the foundational pose estimation stage, multi-source trajectory information is fused to ensure continuity and stability in pose computation. In the local consistency constraint stage, an improved point cloud registration method combined with a neighborhood survey-line constraint mechanism is introduced to enhance geometric consistency among survey lines in feature-sparse regions. At the global optimization stage, a loop closure detection strategy is designed based on topographic statistical features, incorporating adaptive thresholds and correlation metrics to achieve robust introduction of global constraints. By flexibly integrating direct registration and feature-matching strategies according to topographic characteristics, the framework fully leverages the advantages of multi-constraint cooperative optimization. The proposed method is validated by the field data. Experimental results on real lake-trial data show that, relative to the baseline configurations evaluated under identical noise-injection conditions, the MCHS-SLAM framework yields more concentrated consistency-error distributions with markedly shorter large-error tails, and exhibits improved error suppression relative to the reference trajectory. This work presents a methodological framework for high-quality seafloor terrain mapping under heterogeneous terrain conditions, providing a basis for future extensions toward onboard real-time deployment.