PORTER：边缘-云驻留用于持久三维场景图记忆
PORTER: Edge-Cloud Residency for Persistent 3D Scene Graph Memory
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中文总结 AI 辅助
针对机器人长期运行中3DSG内存增长问题，提出PORTER框架，通过边缘-云迁移和ISE指标实现预算感知的负载卸载，在91%字节卸载下保持100%的mR@3。
中文摘要 AI 辅助
近期基于任务驱动和即时性的三维场景图（3DSG）方法通过仅构建或激活与任务相关的信息来减少每个任务的表示。然而，稀疏的每任务工作集并不能限制机器人在其生命周期内的机载内存使用：随着任务变化，为早期任务积累的数据负载可能对当前任务不再相关，但在未来任务中可能再次有用。在反复的任务切换和不断扩展的环境中，保留此类可重用负载会导致本地内存增长，而完全丢弃它们则可能导致未来重复构建相同负载的高昂成本。我们提出PORTER，它将持久性与驻留性解耦：轻量级锚点保留在边缘机器人的有限内存中，而重型对象负载在边缘和云之间迁移。仅凭相关性不足以决定驻留性，因为多个相关负载可能提供冗余信息。因此，我们将每个任务分解为功能需求，并引入不可替代支持擦除（ISE），该指标衡量因卸载而造成的需求覆盖损失。ISE对可替代支持进行折扣，并在需求的剩余覆盖较弱时更强烈地惩罚损失。PORTER通过反复卸载每字节边际ISE最小的负载来构建预算感知的本地工作集。在JITOMA-Bench上的实验评估了PORTER在四种3DSG构建器上的表现。在渐进压缩下，通过91%的负载字节卸载，合并相对mR@3保持在100%。
英文摘要
Recent task-driven and just-in-time 3D Scene Graph (3DSG) methods reduce per-task representations by constructing or activating only task-relevant information. Yet sparse per-task working sets do not bound onboard memory usage over a robot's lifetime: as tasks change, payloads accumulated for earlier tasks may become irrelevant to the current task but can be useful again in future tasks. Over repeated task switches and expanding environments, retaining such reusable payloads causes local memory to grow, whereas discarding them entirely can lead to costly repeated construction of the same payloads later. We introduce PORTER, which decouples persistence from residency: lightweight anchors remain in the limited memory of the edge robot while heavy object payloads migrate between the edge and the cloud. Relevance alone is insufficient for deciding residency because multiple relevant payloads may provide redundant information. We therefore decompose each task into functional requirements and introduce Irreplaceable Support Erasure (ISE), which measures the loss in requirement coverage caused by offloading. ISE discounts replaceable support and penalizes losses more strongly when the remaining coverage of a requirement is weak. PORTER constructs a budget-aware local working set by repeatedly offloading the payload with the smallest marginal ISE per byte. Experiments on JITOMA-Bench evaluate PORTER across four 3DSG builders. Under progressive compression, pooled relative mR@3 remains at 100% through 91% payload-byte offloading.
