基于交叉模态融合的非公路环境道路检测先验校正

Yuru Wang, Yi Sun, Jun Yu Li, Meiping Shi
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引用次数: 0

摘要

道路检测是自动驾驶汽车视觉导航系统的基础。然而,由于复杂的道路外观和模糊的道路结构,在非公路场景中实现鲁棒的道路检测仍然是一个挑战。因此,现有的基于图像的道路检测方法由于缺乏图像和先验参考路径(通过地图注释和GPS定位生成的道路引导)的有效融合,通常无法提取正确的路线。此外,由于GPS定位误差和测绘误差,参考路径并不总是可靠的。为了实现非公路场景下的鲁棒道路检测,我们通过融合参考路径和输入图像提供的交叉模型信息,提出了一种基于先验校正的道路检测网络PR-ROAD。先验和图像这两种异构数据通过交叉关注模块深度融合,形成上下文相互依赖关系。我们在收集的农村、越野和城市数据集上进行实验。实验结果证明了该方法在非结构化和结构化道路上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-modal Fusion-based Prior Correction for Road Detection in Off-road Environments
Road detection plays a fundamental role in the visual navigation system of autonomous vehicles. However, it's still challenging to achieve robust road detection in off-road scenarios due to their complicated road appearances and ambiguous road structures. Therefore, existing image-based road detection approaches usually fail to extract the right routes due to the lack of the effective fusion of the image and prior reference paths(road guidances generated via map annotations and GPS localization). Besides, the reference paths are not always reliable because of GPS localization errors and mapping errors. To achieve robust road detection in off-road scenarios, we propose a prior-correction-based road detection network named PR-ROAD via fusing the cross-model information provided by both the reference path and the input image. These two heterogeneous data, prior and image, are deeply fused by a cross-attention module and formulate contextual inter-dependencies. We conduct experiments in our collected rural, off-road and urban datasets. The experimental results demonstrate the effectiveness of the proposed method both on unstructured and structured roads.
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