InLiER: 中間混合基数構造キーポイントトークン化による学習不要の異種LiDAR位置認識
InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization
異なる特性のLiDAR間で頑健な位置認識を実現するため、構造要素のキーポイントを混合基数トークン化し、3段階の検索で高速かつ高精度に位置を推定する学習不要の手法を提案した。
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著者: Nikolaos Stathoulopoulos, George Nikolakopoulos
分類: cs.CV, cs.RO
原文アブストラクト
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.
