OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
This addresses the problem of storage overhead and real-time adaptability in autonomous navigation for outdoor environments, representing a strong specific gain.
The paper tackles LiDAR place recognition by proposing OPAL, a framework that uses OpenStreetMap as a lightweight prior to bridge domain disparities with LiDAR scans, achieving 15.98% higher recall at 1m threshold and 12x faster inference speed compared to state-of-the-art methods.
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.