Semantic Zone based 3D Map Management for Mobile Robot

Abstract

Mobile robots in large-scale indoor environments, such as hospitals and logistics centers, require accurate 3D spatial representations. However, 3D maps consume substantial memory, making it difficult to maintain complete map data within limited computational resources. Existing SLAM frameworks typically rely on geometric distance or temporal metrics for memory management, often resulting in inefficient data retrieval in spatially compartmentalized environments. To address this, we propose a semantic zone-based 3D map management method that shifts the paradigm from geometry-centric to semantics-centric control. Our approach partitions the environment into meaningful spatial units (e.g., lobbies, hallways) and designates these zones as the primary unit for memory management. By dynamically loading only task-relevant zones into Working Memory (WM) and offloading inactive zones to Long-Term Memory (LTM), the system strictly enforces user-defined memory thresholds. Implemented within the RTAB-Map framework, our method demonstrates substantial reductions in unnecessary signature load/unload cycles and cumulative memory utilization compared to standard approaches. The results confirm that semantic zone-based management ensures stable, predictable memory usage while preserving map availability for navigation. Code is available at: https://github.com/huichangs/rtabmap/tree/segment