Cross-Modal Registration Using Adaptive Modeling in Infrastructure-Based Vehicle Localization

Infrastructure-based vehicle localization, in comparison to single-agent approaches, offers several advantages including reduced system cost, extended perception range, enhanced data fusion capabilities, and energy savings. Many conventional approaches impose limitations on the types of objects due to the need for specific object-end modifications, such as applying perceptual markers like color-labeled plates and reflective balls. LiDAR presents a solution in terms of object arbitrariness, as it addresses the challenges of feature-free object modeling and continuous registration. However, achieving complete environmental coverage with LiDAR remains prohibitively expensive, particularly in extensive areas. Hence, this study proposes a cross-modal localization approach using adaptive modeling, employing LiDAR for object modeling and cost-effective sensor cameras for object tracking through image-point-cloud registration. Accurate correspondence between the model and observation can be estimated in real-time. The experiments are conducted in a typical scenario that requires adaptive modeling: Autonomous Valet Parking (AVP). Results demonstrate that the proposed system achieves comparable performance with significantly reduced system costs, highlighting its potential for large-scale deployment.