发表机构
ST Engineering; National University of Singapore（新科工程; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出语义感知预测建图，利用门语义线索改善机器人探索与导航中模糊区域的占据补全，实验显示局部门区域L1误差显著降低，F1和IoU提升至1.0。
AI 中文摘要
预测建图可以通过从部分占据观测中估计未见的几何布局来支持机器人探索与导航。然而，仅基于占据信息的表示可能无法区分具有相似几何形状但语义不同的结构。这对于室内门尤其相关，门可能像墙壁一样显示为占据单元，但可能指示观测区域之外可能相连的房间或走廊。本研究探讨了语义门线索是否能在这类模糊区域周围改善预测性几何占据建图。我们修改了CogniPlan数据集的一个子集，在部分占据图中插入由门引起的模糊性，同时保持真实布局不变。我们将一个仅基于几何的对照模型与一个在同一修改数据集上训练的语义线索模型进行比较，其中语义线索模型接收额外的门通道。评估使用L1误差、F1分数和交并比（IoU），覆盖整个地图和一个10像素的门区域掩码。全图性能在模型之间大致相似，但局部门区域结果显示明显的定性改进：L1从0.004342降至0.000025，而F1和IoU分别从0.031311和0.015905提升至1.000000和1.000000。这些结果表明，在仅凭几何观测难以判断的区域，语义线索可以改善预测性占据补全。
英文摘要
Predictive mapping can support robotic exploration and navigation by estimating unseen geometric layouts from partial occupancy observations. However, occupancy-only representations may fail to distinguish semantically different structures with similar geometry. This is particularly relevant for indoor doors, which may appear as occupied cells like walls but indicate possible connected rooms or corridors beyond the observed region. This work investigates whether semantic door cues improve predictive geometric occupancy mapping around such ambiguous regions. We modify a subset of the CogniPlan dataset by inserting door-induced ambiguities into partial occupancy maps while keeping the ground-truth layouts unchanged. We compare a geometry-only control model with a semantic-cued model trained on the same modified dataset, where the semantic-cued model receives an additional door channel. Evaluation uses L1 error, F1 score, and Intersection over Union (IoU) over both the full map and a 10-pixel door-region mask. Full-map performance remains broadly similar between models, but localized door-region results show a clear qualitative improvement: L1 decreases from 0.004342 to 0.000025, while F1 and IoU improve from 0.031311 and 0.015905 to 1.000000 and 1.000000, respectively. These results suggest that semantic cues can improve predictive occupancy completion in regions where geometric observations alone are ambiguous.
CommentsAccepted at IEEE TENCON 2026
