Multi-Camera Simultaneous Localization and Mapping for Unmanned Systems: A Survey

Autonomous navigation in unmanned systems increasingly relies on robust perception and mapping capabilities under large-scale, dynamic, and unstructured environments. Multi-camera simultaneous localization and mapping (MCSLAM) has emerged as a promising solution due to its improved field-of-view coverage, redundancy, and robustness compared to single-camera systems. However, the deployment of MCSLAM introduces several technical challenges that remain insufficiently addressed in existing literature. These challenges include the high-dimensional nature of multi-view visual data, the computational cost associated with multi-view geometry and large-scale bundle adjustment, and the strict requirements on camera calibration, temporal synchronization, and geometric consistency across heterogeneous viewpoints. This survey provides a comprehensive review of recent advances in MCSLAM for unmanned systems, categorizing existing approaches based on system configuration, field-of-view overlap, calibration strategies, and optimization frameworks. We further analyze common failure modes, evaluate representative algorithms, and identify emerging research trends toward scalable, real-time, and uncertainty-aware MCSLAM in complex operational environments.