Task-oriented visual SLAM: a comprehensive map classification framework for dynamic indoor robot manipulation

Visual Simultaneous Localization and Mapping (vSLAM) is fundamental to enabling robotic mobile manipulation-i.e., the seamless integration of navigation, perception, and dexterous interaction with objects in unstructured environments. Yet current vSLAM research largely lacks a principled, task-oriented framework for map classification, resulting in suboptimal map representations that hinder robustness and efficiency in dynamic indoor settings. To bridge this gap, we propose a purpose-driven taxonomy of vSLAM maps specifically designed for mobile manipulation tasks. This taxonomy comprises four complementary categories: geometric 3D maps, semantic maps, object-level maps, and hybrid maps-each distinguished by its representational granularity, functional scope, and suitability for downstream manipulation primitives. We provide a systematic comparative analysis of their construction pipelines, underlying technical assumptions, and real-world deployment contexts, evaluating them rigorously across three critical dimensions: environmental adaptability, pose estimation accuracy, and real-time computational feasibility. Finally, we synthesize key limitations in existing approaches and identify concrete, high-impact directions for future work-including tight coupling between mapping semantics and manipulation affordances, and scalable learning-based map fusion.