利用全景图像中的地平线实现无人船厘米级定位
HorizonNet for visual terrain navigation
- 通过双卷积网络提取并校正地平线，估计相机姿态
- 在傅里叶域用MOSSE滤波器匹配地平线与数字高程图，定位精度达GPS级别
- 适用于复杂岛礁海域，无需依赖卫星信号的自主导航
本文研究无人水面艇（USV）在沿海或群岛区域的位置估计问题。提出一种方法：从USV周围360度全景图像中提取地平线，设计首个卷积神经网络（CNN）近似检测图像中的地平线并隐式推断相机俯仰角和翻滚角。将全景图像进行姿态补偿以生成近似水平视角图像，再使用第二个CNN提取像素级地平线。随后，在傅里叶域中利用MOSSE相关滤波器将提取的地平线与数字高程模型（DEM）数据进行匹配，通过搜索区域内最大相关得分确定USV位置。在群岛实地试验中，该方法实现了接近GPS精度的位置估计。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。