发表机构
Anyverse Dynamics（Anyverse Dynamics）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对室内LiDAR地点识别中姿态和高度变化导致的检索困难，提出无需训练的UpDown-SC描述子，通过重力归一化和双包络表示，在实验中显著提升检索可靠性并支持定位。
AI 中文摘要
LiDAR地点识别是回环检测和全局重定位的关键前端，然而当建图与查询会话之间的姿态或传感器安装高度发生变化时，室内检索仍然困难。扫描上下文在每个极坐标单元中存储最大高度；在室内，宽阔的天花板可能抑制区分相邻房间和走廊的较低及中层几何结构。我们提出UpDown-SC，一种无需训练的极坐标描述子，它首先对重力进行归一化，然后表示两个互补表面：较低/中层结构的上包络和顶部结构的下包络。它们的物理分割仅从单元平衡的地图高度分布中估计一次，并被每个查询复用。一种掩码感知的非均匀双通道距离保留了判别性的较低层证据，同时限制其对跨会话变化的敏感性，而不将未观测单元视为零高度测量。保留传统的扫描上下文候选筛选和圆形偏航对齐，因此检索到的假设直接初始化几何验证。在重复室内会话、安装高度变化、混合室外到室内轨迹以及室外迁移序列上的实验表明，在室内和安装高度变化的会话上，首选检索更加可靠。配对检验发现，在内部会话上相比扫描上下文有显著提升。在基于阈值的接受下，UpDown-SC还给出最佳或次佳的F1max和AUPR，同时保持轻量级CPU前端。连续重放确认检索到的假设支持度量先验地图定位。代码和评估工件：此https URL。
英文摘要
LiDAR place recognition is a key front end for loop closure and global relocalization, yet indoor retrieval remains difficult when attitude or sensor mounting height changes between mapping and query sessions. Scan Context stores the maximum height in each polar cell; indoors, broad ceilings can suppress the lower and mid-level geometry that distinguishes adjacent rooms and corridors. We present UpDown-SC, a training-free polar descriptor that first canonicalizes gravity and then represents two complementary surfaces: the upper envelope of lower/middle structures and the lower envelope of overhead structures. Their physical split is estimated once from a cell-balanced map height distribution and reused by every query. A mask-aware, non-uniform two-channel distance retains discriminative lower-level evidence while limiting sensitivity to its cross-session variation, without treating unobserved cells as zero-height measurements. Conventional Scan Context shortlisting and circular yaw alignment are retained, so retrieved hypotheses directly initialize geometric verification. Experiments across repeated indoor sessions, mounting-height changes, mixed outdoor-to-indoor trajectories, and an outdoor transfer sequence show more reliable first-choice retrieval on the indoor and mounting-height-varied sessions. A paired test finds a significant gain over Scan Context on the in-house sessions. UpDown-SC also gives the best or second-best F1max and AUPR under threshold-based acceptance while retaining a lightweight CPU front end. Continuous replay confirms that the retrieved hypotheses support metric prior-map localization. Code and evaluation artifacts: https://github.com/jiejie567/updown-sc.
Comments8 pages, 7 figures, 2 tables. Code and evaluation artifacts: https://github.com/jiejie567/updown-sc
