无需训练的激光雷达定位方法，通过结构化关键点编码实现跨传感器高精度识别。
InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization
- 基于高度分层的关键点，用混合进制编码融合几何特征，生成小于2KB的紧凑表示
- 在HeLiPR数据集上性能领先，跨传感器配置下优于学习型基线模型
- 适合多传感器融合场景，尤其适用于无标注数据或需快速部署的机器人系统
激光雷达定位支持回环检测、重定位及多智能体地图管理。随着机器人平台集成具有不同视场、分辨率和扫描模式的激光雷达，现有描述子因与传感器特性强耦合而性能下降。本文提出InLiER，一种无需训练的流水线，包含中间标记化步骤：从结构元素中提取高度分层的关键点，赋予混合进制标记ID，编码高度、径向距离、局部形状和方位角等局部3D几何信息，形成紧凑的亚2KB表示。同一词表在三个检索阶段重用：高度穹顶直方图交集用于快速旋转不变性筛选，二值位掩码对齐用于偏航估计与重排序，令牌引导几何验证用于6-DoF位姿估计。InLiER在HeLiPR数据集及真实环境实验中均达到现代手工设计方法的最先进水平，并在多数跨传感器配置下超越学习型基线。
原文摘要 · Abstract (English)
LiDAR place recognition supports loop closure, relocalization, and multi-agent map management. As robotic platforms increasingly combine LiDARs with different fields of view, resolutions, and scanning patterns, existing descriptors degrade because they are tightly coupled to sensor-specific characteristics. We present InLiER, a learning-free pipeline based on an intermediate tokenization step. Height-sliced keypoints from structural elements receive mixed-radix token IDs encoding height, radial distance, local shape, and azimuth from local 3D geometry, in a compact sub-2KB representation. The same vocabulary is reorganized across three retrieval stages: height-ceiling histogram intersection for fast rotation-invariant shortlisting, binary bitmask alignment for yaw estimation and reranking, and token-guided geometric verification for 6-DoF pose estimation. InLiER achieves state-of-the-art performance on the HeLiPR dataset and in real-world field experiments, among modern handcrafted methods and outperforms the learning-based baseline on most cross-sensor configurations.
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