李晨旻;姚剑;龚烨;刘欣怡;

• 1:武汉大学遥感信息工程学院

摘要(Abstract)：

针对目前视觉定位方法中使用人工特征的限制，提出了一种基于全卷积编解码网络的视觉定位方法。该方法将场景点3D坐标映射到图像的BGR(blue-green-red)通道，建立了图像到场景的直接联系，并通过全卷积编解码网络学习图像与场景结构的关系。给出一张图像，网络可以预测其每个像素点对应的3D点在当前场景世界坐标系的坐标；然后结合RANSAC(random sample consensus)和PnP(perspective-n-point)算法求解位姿并优化，得到最终的相对位姿。在7-Scenes数据集上的实验结果表明本文方法可实现厘米级的高精度定位，并且相比其他基于深度学习的方法，该方法在保证精度的同时，模型尺寸更小。

关键词(KeyWords)： 视觉定位;场景构建;姿态估计;深度学习

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（41571436）

作者(Authors): 李晨旻;姚剑;龚烨;刘欣怡;

DOI: 10.14188/j.2095-6045.2020072

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