Autonomous driving paper index
BEV-LOC: Real-Time and Lightweight Cross-View Localization via Online BEV Mapping
One-line summary
This paper presents a deep learning and classical computer vision framework for cross-view geolocalization using 360-degree multi-perspective view (PV) images and an offline global map.
Engineering notes
Our architecture achieves real-time performance at 30 FPS without the need for high-end GPU hardware and delivers a high positioning accuracy of 1.2 meters.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract. This paper presents a deep learning and classical computer vision framework for cross-view geolocalization using 360-degree multi-perspective view (PV) images and an offline global map. Recent studies on cross-view geolocalization typically rely on deep learning models to localize panoramic PV images by matching them with reference satellite imagery. However, such approaches face practical limitations in real-world deployments, due to their dependence on large-scale GPU resources and the need to store extensive satellite image datasets. To address these challenges, we propose BEV-LOC, a lightweight and real-time cross-view geolocalization method. BEV-LOC employs Bird’s Eye View (BEV) encoder that learns to transform 360-degree multi-PV images into a local high-definition (HD) BEV map. The localization is then performed using Intersection Over Union (IoU)-based template matching with an offline global map. Our architecture achieves real-time performance at 30 FPS without the need for high-end GPU hardware and delivers a high positioning accuracy of 1.2 meters.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments