用于视觉地形导航的HorizonNet
HorizonNet for visual terrain navigation
- Linköping University（林雪平大学）
- Inception Institute of Artificial Intelligence（Inception人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
该研究针对沿海或群岛区域无人水面艇的位置估计问题，提出基于双CNN架构与MOSSE相关滤波器的视觉地形导航方法，在野外试验中实现了GPS级精度的位置估计。
中文摘要 AI 辅助
本文研究在沿海或群岛区域作业的无人水面艇（USV）的位置估计问题。我们提出一种位置估计方法：在无人水面艇周围的360度全景图像中提取地平线。设计一个CNN架构以确定图像中的近似地平线，并隐式确定相机朝向（俯仰角和横滚角）。将全景图像进行变形以补偿相机朝向，生成近似水平相机视角的图像。设计第二个CNN架构以在变形后的图像中提取像素级地平线。提取的地平线与数字高程模型（DEM）数据在傅里叶域中使用MOSSE相关滤波器进行关联。最后，我们确定搜索区域内最大相关得分的位置以估计无人水面艇的位置。在群岛的野外试验中开展了全面实验，我们的方法取得了良好结果，实现了GPS级精度的位置估计。
英文摘要
This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degree panoramic image around the USV. We design a CNN architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model (DEM) data in the Fourier domain using a MOSSE correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in a field trial in the archipelago. Our approach provides promising results by achieving position estimates with GPS-level accuracy.
