发表机构
University of Turku; Fudan University（图尔库大学; 复旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
P-POSEMEM通过存储不可变观测和保留消元条件，在SLAM位姿图重写下保持语言接地一致性，以Dproj度量并显著减少目标翻转。
AI 中文摘要
遵循语言指令的机器人需要其语义记忆在底层SLAM位姿图被优化、闭环和压缩时，持续命名同一物理对象。将每个检测提交到世界坐标的地图无法做到这一点：闭环会移动测量该对象的锚点，或求解器会边缘化该锚点，导致查询选择不同的对象，尽管两个图表示相同的后验。P-POSEMEM将每个观测存储为其出生关键帧处的不可变事件，保留每个被边缘化关键帧的贝叶斯树消元条件，并在重建的位姿、锚点和身份联合后验上积分语义似然。Dproj是推理等价的全图与边缘化图的语言目标分布之间的全变差缺陷，直接衡量这一点。在40个HM3DSem场景和112,000次查询中，P-POSEMEM复现了全图预言机（Dproj=0），并相对于所有减少记忆的基线减少了目标翻转。在一次八次运行的活动（其761次闭环将地图重写最多47米）中，当消元跟随闭环时，Dproj保持在10^-13以下，目标翻转0/288，而所有消融和插入时提交的坐标都会翻转目标；在实时有界求解器下，相同记忆翻转23/288，而冻结坐标翻转53次。预注册的阴性对照被Dproj检测到，同时校准误差和导航成功率不变，表明这些度量捕获不同的失败模式。检索由共享的冻结检测器固定，将增益隔离到记忆一致性。代码和数据：此https URL。
英文摘要
A robot following language instructions needs its semantic memory to keep naming the same physical object while the SLAM pose graph underneath is optimized, loop-closed and compressed. Maps committing each detection to a world coordinate cannot: a closure moves the anchor it was measured from, or the solver marginalizes that anchor, and the query then selects a different object although both graphs represent the same posterior. P-POSEMEM stores each observation as an immutable event at its birth keyframe, retains the Bayes-tree elimination conditional of every marginalized keyframe, and integrates the semantic likelihood over the reconstructed joint posterior of poses, anchors and identities. Dproj, the total-variation defect between the language-goal distributions of inference-equivalent full and marginalized graphs, measures this directly. Over 40 HM3DSem scenes and 112,000 queries, P-POSEMEM reproduces the full-graph oracle (Dproj = 0) and reduces goal flips against every memory-reducing baseline. On an eight-run campaign whose 761 closures rewrote the map by up to 47 m, Dproj stays below 10^-13 with 0/288 goal flips when elimination follows the closures, where every ablation and a coordinate committed at insertion flip goals it does not; under a live bounded solver the same memory flips 23/288 against 53 for that frozen coordinate. A pre-registered negative control is detected by Dproj while leaving calibration error and navigation success unchanged, indicating that these measures capture distinct failure modes. Retrieval is held fixed by a shared frozen detector, isolating the gain to memory consistency. Code and data: https://anonymous.4open.science/r/posemem-2328/.
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