Challenges and Advances in SLAM-Based Inspection for Low-Texture and Confined Environments: A Systematic Review

This systematic review investigates recent advancements in SLAM-based navigation and mapping for confined, low-texture environments such as pipelines and industrial facilities. Emphasis is placed on hybrid sensor integration, calibration strategies, and advanced feature extraction techniques to address challenges including scarce visual cues and GPS-denied conditions. The findings highlight the superior robustness of tightly coupled multi-sensor architectures and underscore the critical role of accurate calibration in ensuring data fusion consistency. Despite notable progress, issues such as real-time processing limitations and sensor drift persist. Future research should focus on adaptive, AI-driven frameworks and the development of scalable, self-calibrating SLAM systems to enhance autonomous inspection in complex environments.