论文标题
端到端异质传感器测量匹配的深相相关
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
论文作者
论文摘要
定位的关键步骤是将当前观察结果与地图匹配。当两个传感器方式显着不同时，匹配就会具有挑战性。在本文中，我们提出了一个端到端的深层相关网络（DPCN），以匹配异质传感器测量。在DPCN中，主要组件是一个基于可区分相关的估计器，它将姿势错误回到可学习的特征提取器中，该姿势错误解决了没有直接共同特征进行监督的问题。此外，它消除了以前的某些方法中详尽的评估，从而提高了效率。借助可解释的建模，该网络具有轻度加权，有望获得更好的概括。我们在模拟数据和空气地面数据集上评估了系统，该数据集由卫星或空中机器人获得的异质传感器图像和空中图像组成。结果表明，我们的方法能够匹配异质传感器测量值，表现优于传统相关性和其他基于学习的方法。代码可在https://github.com/jessychen1016/dpcn上找到。
The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this paper, we present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. In DPCN, the primary component is a differentiable correlation-based estimator that back-propagates the pose error to learnable feature extractors, which addresses the problem that there are no direct common features for supervision. Also, it eliminates the exhaustive evaluation in some previous methods, improving efficiency. With the interpretable modeling, the network is light-weighted and promising for better generalization. We evaluate the system on both the simulation data and Aero-Ground Dataset which consists of heterogeneous sensor images and aerial images acquired by satellites or aerial robots. The results show that our method is able to match the heterogeneous sensor measurements, outperforming the comparative traditional phase correlation and other learning-based methods. Code is available at https://github.com/jessychen1016/DPCN .
