用语义区域管理地图，让机器人在内存受限下稳定运行大模型。
Semantic Zone-Based Map Management for Stable AI-Integrated Mobile Robots
- 按房间走廊等语义区域组织关键帧，智能调度地图数据。
- 内存压力下仍保持定位稳定，处理速度提升3.3 tokens/s。
- 适合资源受限的AI机器人，尤其需要大模型交互的场景。
大型AI模型（如视觉语言模型和大语言模型）与三维稠密地图的结合，使移动机器人能在丰富空间上下文中提供更强大的交互服务。然而，在边缘机器人上同时部署重型AI模型和稠密地图面临严格内存预算的挑战。当内存超限，关键帧无法及时加载，会降低定位稳定性并干扰模型性能。本文提出基于语义区域的地图管理方法，在内存约束下稳定利用稠密地图。将关键帧关联至语义室内区域（如房间、走廊），在语义区域层面进行关键帧管理，优先保留空间相关地图内容，同时满足内存限制。该方法显著降低关键帧频繁加载卸载次数和内存占用。我们在大规模模拟室内环境及NVIDIA Jetson Orin Nano上评估了该方法，结合Qwen3.5:0.8b模型，相比几何地图管理策略，吞吐量提升3.3 tokens/s，延迟降低21.7%。此外，几何策略在内存压力下出现内存溢出失败和执行停滞，而本方法完全消除这些问题，维持定位稳定并实现鲁棒的VLM运行。结果表明，该方法能高效支持内存受限的AI集成机器人使用稠密地图。代码已开源：https://github.com/huichangs/rtabmap/tree/segment
原文摘要 · Abstract (English)
Recent advances in large AI models (VLMs and LLMs) and joint use of the 3D dense maps, enable mobile robots to provide more powerful and interactive services grounded in rich spatial context. However, deploying both heavy AI models and dense maps on edge robots is challenging under strict memory budgets. When the memory budget is exceeded, required keyframes may not be loaded in time, which can degrade the stability of position estimation and interfering model performance. We proposes a semantic zone-based map management approach to stabilize dense-map utilization under memory constraints. We associate keyframes with semantic indoor regions (e.g., rooms and corridors) and keyframe management at the semantic zone level prioritizes spatially relevant map content while respecting memory constraints. This reduces keyframe loading and unloading frequency and memory usage. We evaluate the proposed approach in large-scale simulated indoor environments and on an NVIDIA Jetson Orin Nano under concurrent SLAM-VLM execution. With Qwen3.5:0.8b, the proposed method improves throughput by 3.3 tokens/s and reduces latency by 21.7% relative to a geometric map-management strategy. Furthermore, while the geometric strategy suffers from out-of-memory failures and stalled execution under memory pressure, the proposed method eliminates both issues, preserving localization stability and enabling robust VLM operation. These results demonstrate that the proposed approach enables efficient dense map utilization for memory constrained, AI-integrated mobile robots. Code is available at: https://github.com/huichangs/rtabmap/tree/segment
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