🤖 AI Summary
For large-scale indoor environments (e.g., hospitals, logistics centers), mobile robots face excessive memory consumption in 3D mapping and inefficient cross-region retrieval due to conventional geometry- or time-driven SLAM memory management. This paper proposes a semantic-region-driven 3D map memory management method. It parses the environment into functionally explicit semantic units (e.g., corridors, lobbies), constructs a region graph at the semantic-region granularity, and designs a two-level memory scheduling mechanism integrated into RTAB-Map—combining real-time semantic segmentation with strict memory-threshold control. To our knowledge, this is the first work to shift the memory management paradigm from geometry/time-driven to semantic-driven. The approach significantly reduces redundant signature loading/unloading frequency and cumulative memory footprint while preserving navigational map usability, thereby achieving stable, predictable, and task-oriented memory utilization.
📝 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