MaPLocator: Map-Prior Enhanced End-to-End Vehicle Localization with Coarse-to-Fine Pose Refinement

Engineering notes

Extensive experiments on the nuScenes dataset demonstrate that MaPLocator achieves state-of-the-art localization performance, improving longitudinal and yaw accuracy by 47% and 31%, respectively, compared with the previous leading method BEV-Locator.

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

High-precision vehicle localization is essential for high-level autonomous driving. Recent end-to-end localization frameworks have shown promising capabilities in pose estimation, yet their performance is still limited by suboptimal visual perception and insufficient cross-modal alignment, leading to degraded accuracy and robustness under challenging visual conditions. To address these issues, we propose MaPLocator, a novel end-to-end visual-only localization network that performs cross-modal localization using surrounding cameras and high-definition (HD) maps. In the network, a novel Map Prior-Guided Perception Enhancement Module is proposed, which leverages misaligned HD map priors to improve visual bird’s-eye-view (BEV) features while generating a coarse pose estimation. Subsequently, a cross-modal Transformer module is employed to further refine the estimated pose so that the whole localization procedure is performed in a coarse-to-fine strategy, yielding high-precision pose estimation. Extensive experiments on the nuScenes dataset demonstrate that MaPLocator achieves state-of-the-art localization performance, improving longitudinal and yaw accuracy by 47% and 31%, respectively, compared with the previous leading method BEV-Locator.

Links and sources

• Official / arXiv page

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