论文标题
范围传感器和高架图像之间的自我监督本地化
Self-Supervised Localisation between Range Sensors and Overhead Imagery
论文作者
论文摘要
当先前的传感器图无法使用时，公开可用的卫星图像可能是无处不在，便宜且功能强大的工具。但是，卫星图像由于其截然不同的方式而与地面范围传感器的数据不直接可比。我们提出了一种学识渊博的度量定位方法，该方法不仅可以处理方式差异，而且训练便宜，以自我监督的方式学习，而没有计量准确的地面真理。通过跨多个现实世界数据集进行评估，我们证明了用于各种传感器配置的方法的鲁棒性和多功能性。我们特别注意使用毫米波雷达，由于它与场景的复杂相互作用及其对天气和照明的免疫力，这是一种引人入胜且有价值的用例。
Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable. However, satellite images are not directly comparable to data from ground range sensors because of their starkly different modalities. We present a learned metric localisation method that not only handles the modality difference, but is cheap to train, learning in a self-supervised fashion without metrically accurate ground truth. By evaluating across multiple real-world datasets, we demonstrate the robustness and versatility of our method for various sensor configurations. We pay particular attention to the use of millimetre wave radar, which, owing to its complex interaction with the scene and its immunity to weather and lighting, makes for a compelling and valuable use case.
