InLiER：通过中间混合基数结构关键点令牌化实现无学习的异构激光雷达地点识别
InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization
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中文总结 AI 辅助
研究针对机器人平台结合多种激光雷达致现有描述符性能下降的问题，提出无学习管道InLiER，通过混合基数令牌ID编码关键点信息，经三个检索阶段处理，在相关数据集和实验中表现出色，优于基于学习的基线。
中文摘要 AI 辅助
激光雷达地点识别支持闭环、重新定位和多智能体地图管理。随着机器人平台越来越多地将具有不同视野、分辨率和扫描模式的激光雷达结合起来，现有描述符因与传感器特定特征紧密耦合而性能下降。我们提出了InLiER，这是一个基于中间令牌化步骤的无学习管道。来自结构元素的高度切片关键点接收混合基数令牌ID，其编码来自局部3D几何的高度、径向距离、局部形状和方位，以紧凑的小于2KB表示。相同词汇表在三个检索阶段重新组织：用于快速旋转不变初选的高度上限直方图交集、用于偏航估计和重新排序的二进制位掩码对齐，以及用于6自由度姿态估计的令牌引导几何验证。InLiER在HeLiPR数据集和实际现场实验中，在现代手工方法中实现了最先进的性能，并且在大多数跨传感器配置上优于基于学习的基线。
英文摘要
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.
