Abstract
Accurate localisation in planetary robotics enables the advanced autonomy required to support the increased scale and scope of future missions. The successes of the Ingenuity helicopter and multiple planetary orbiters lay the groundwork for future missions that use ground-aerial robotic teams. In this paper, we consider rovers using machine learning to localise themselves in a local aerial map using limited field-of-view monocular ground-view RGB images as input. A key consideration for machine learning methods is that real space data with ground-truth position labels suitable for training is scarce. In this work, we propose a novel method of localising rovers in an aerial map using cross-view-localising dual-encoder deep neural networks. We leverage semantic segmentation with vision foundation models and high volume synthetic data to bridge the domain gap to real images. We also contribute a new cross-view dataset of real-world rover trajectories with corresponding ground-truth localisation data captured in a planetary analogue facility, plus a high volume dataset of analogous synthetic image pairs. Using particle filters for state estimation with the cross-view networks allows accurate position estimation over simple and complex trajectories based on sequences of ground-view images.
Abstract (translated)
行星机器人中的精确定位能够实现未来任务扩大规模和范围所需的高度自主性。Ingenuity直升机和多个环绕器的成功为未来使用地面-空中机器人团队的任务奠定了基础。在本文中，我们考虑了一种方法，即利用有限视野的单目地面RGB图像作为输入，通过机器学习使探测车能够在局部的空中地图上定位自己。 对于机器学习方法而言，关键挑战在于真实空间数据及其带有位置标签的数据量较少，这些标签适用于训练过程。为此，在本工作中，我们提出了一种新的方法，即利用跨视图定位双编码深度神经网络来对在空中地图中进行探测车的定位。我们采用语义分割和视觉基础模型，并使用大量合成数据来弥合与真实图像之间的领域差距。此外，我们还贡献了一个新建立的真实世界数据集，包括在行星类地设施中捕获的实际轨迹及其相应的精确位置标签，以及大量的类似合成图像对的数据集。 利用粒子滤波器进行状态估计，并结合跨视图网络，可以基于一系列地面视角的图像实现简单和复杂轨迹上的准确位置估算。
URL
https://arxiv.org/abs/2601.09107
