OPAL: 可視性を考慮したLiDAR-OpenStreetMap間の適応的放射状融合による場所認識
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
LiDARスキャンとOpenStreetMapの間のドメインギャップを可視性マスクと適応的放射状融合で橋渡しし、軽量な地図事前情報を用いた高精度・高速な場所認識を実現した。
著者: Shuhao Kang, Martin Y. Liao, Yan Xia, Olaf Wysocki, Boris Jutzi, Daniel Cremers
分類: cs.CV, cs.RO
原文アブストラクト
LiDAR place recognition is a critical capability for autonomous navigation and cross-modal localization in large-scale outdoor environments. Existing approaches predominantly depend on pre-built 3D dense maps or aerial imagery, which impose significant storage overhead and lack real-time adaptability. In this paper, we propose OPAL, a novel framework for LiDAR place recognition that leverages OpenStreetMap (OSM) as a lightweight and up-to-date prior. Our key innovation lies in bridging the domain disparity between sparse LiDAR scans and structured OSM data through two carefully designed components. First, a cross-modal visibility mask that identifies observable regions from both modalities to guide feature alignment. Second, an adaptive radial fusion module that dynamically consolidates radial features into discriminative global descriptors. Extensive experiments on KITTI and KITTI-360 datasets demonstrate OPAL's superiority, achieving 15.98% higher recall at 1m threshold for top-1 retrieved matches, along with 12x faster inference speed compared to the state-of-the-art approach. Code and data are publicly available at: https://github.com/kang-1-2-3/OPAL.
