OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion

• URL: http://arxiv.org/abs/2504.19258v2
• Date: Wed, 30 Apr 2025 10:06:51 GMT
• Title: OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
• Authors: Shuhao Kang, Martin Y. Liao, Yan Xia, Olaf Wysocki, Boris Jutzi, Daniel Cremers,
• Abstract summary: OPAL is a novel network for LiDAR place recognition that leverages OpenStreetMap (OSM) as a lightweight and up-to-date prior.<n>Our key innovation lies in bridging the domain disparity between sparse LiDAR scans and structured OSM data through two carefully designed components.<n>Experiments on the KITTI and KITTI-360 datasets demonstrate OPAL's superiority, achieving 15.98% higher recall at @1m threshold for top-1 retrieved matches.
• Score: 33.87605068407066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 network 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 maximal observable regions from both modalities to guide feature learning. Second, an adaptive radial fusion module that dynamically consolidates radial features into discriminative global descriptors. Extensive experiments on the 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 datasets will be publicly available.

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