Humans can orient themselves in their 3D environments using simple 2D maps.
Differently, algorithms for visual localization mostly rely on complex 3D point
clouds that are expensive to build, store, and maintain over time. We bridge
this gap by introducing OrienterNet, the first deep neural network that can
localize an image with sub-meter accuracy using the same 2D semantic maps that
humans use. OrienterNet estimates the location and orientation of a query image
by matching a neural Bird's-Eye View with open and globally available maps from
OpenStreetMap, enabling anyone to localize anywhere such maps are available.
OrienterNet is supervised only by camera poses but learns to perform semantic
matching with a wide range of map elements in an end-to-end manner. To enable
this, we introduce a large crowd-sourced dataset of images captured across 12
cities from the diverse viewpoints of cars, bikes, and pedestrians. OrienterNet
generalizes to new datasets and pushes the state of the art in both robotics
and AR scenarios. The code and trained model will be released publicly.

论文提出了 OrienterNet，这是一个可以使用 2D 语义地图对图像进行亚米级别本地化的深度神经网络，通过匹配神经鸟瞰与来自 OpenStreetMap 的开放和全球可用地图，可以使任何人在这些地图提供的地方进行本地化。 OrienterNet 通过监督相机姿势进行训练，但学习以端到端的方式执行与地图元素的语义匹配，具有广泛的泛化能力，代码和训练模型将公开发布。