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

2025-04-27

AI Summary

To address the high computational cost and poor real-time adaptability of LiDAR-based place recognition in large-scale outdoor environments—typically reliant on expensive 3D maps or aerial imagery—this paper proposes a lightweight and efficient approach leveraging dynamically updated OpenStreetMap (OSM) as a structured geometric prior, eliminating dense 3D reconstruction and remote sensing imagery. Our core contributions include: (1) a visibility-aware cross-modal masking mechanism and (2) an adaptive radial feature fusion strategy, jointly bridging the representation gap between sparse LiDAR point clouds and vectorized OSM. We further integrate multi-scale radial feature extraction with end-to-end contrastive learning. Evaluated on enhanced KITTI and KITTI-360 benchmarks, our method achieves a 15.98% improvement in top-1 recall at a 1-meter threshold and operates at 12× the inference speed of current state-of-the-art methods.

Technology Category

• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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 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: a cross-modal visibility mask that identifies maximal observable regions from both modalities to guide feature learning, and an adaptive radial fusion module that dynamically consolidates multiscale radial features into discriminative global descriptors. Extensive experiments on the augmented KITTI and KITTI-360 datasets demonstrate OPAL's superiority, achieving 15.98% higher recall at @1m threshold for top-1 retrieved matches while operating at 12x faster inference speeds compared to state-of-the-art approaches. Code and datasets are publicly available at: https://github.com/WHU-USI3DV/OPAL .

Problem

• Bridges LiDAR and OpenStreetMap for place recognition
• Reduces storage needs and improves real-time adaptability
• Enhances accuracy and speed in cross-modal localization

Innovation

• Uses OpenStreetMap as lightweight prior
• Cross-modal visibility mask guides learning
• Adaptive radial fusion for global descriptors