One of the most critical topics in autonomous driving or ride-sharing
technology is to accurately localize vehicles in the world frame. In addition
to common multi-view camera systems, it usually also relies on industrial grade
sensors, such as LiDAR, differential GPS, high precision IMU, and etc. In this
paper, we develop an approach to provide an effective solution to this problem.
We propose a method to train a geo-spatial deep neural network (CNN+LSTM) to
predict accurate geo-locations (latitude and longitude) using only ordinary
ground imagery and low accuracy phone-grade GPS. We evaluate our approach on
the open dataset released during ACM Multimedia 2017 Grand Challenge. Having
ground truth locations for training, we are able to reach nearly lane-level
accuracy. We also evaluate the proposed method on our own collected images in
San Francisco downtown area often described as "downtown canyon" where consumer
GPS signals are extremely inaccurate. The results show the model can predict
quality locations that suffice in real business applications, such as
ride-sharing, only using phone-grade GPS. Unlike classic visual localization or
recent PoseNet-like methods that may work well in indoor environments or
small-scale outdoor environments, we avoid using a map or an SFM
(structure-from-motion) model at all. More importantly, the proposed method can
be scaled up without concerns over the potential failure of 3D reconstruction.

本文提出了一种利用普通地面图像和低精度手机 GPS 训练地理空间深度神经网络以准确预测车辆地理位置 (纬度和经度) 的方法。我们在一个公开数据集和我们自己采集的在雾都旧金山市中心的图像上验证了该方法，结果表明模型可以在实际业务应用中足够预测质量位置，同时避免了使用地图或 SFM 模型的当今经典视觉定位或姿态估计方法所遇到的问题。