  Many applications such as autonomous navigation, urban planning and asset
monitoring, rely on the availability of accurate information about objects and
their geolocations. In this paper we propose to automatically detect and
compute the GPS coordinates of recurring stationary objects of interest using
street view imagery. Our processing pipeline relies on two fully convolutional
neural networks: the first segments objects in the images while the second
estimates their distance from the camera. To geolocate all the detected objects
coherently we propose a novel custom Markov Random Field model to perform
objects triangulation. The novelty of the resulting pipeline is the combined
use of monocular depth estimation and triangulation to enable automatic mapping
of complex scenes with multiple visually similar objects of interest. We
validate experimentally the effectiveness of our approach on two object
classes: traffic lights and telegraph poles. The experiments report high object
recall rates and GPS accuracy within 2 meters, which is comparable with the
precision of single-frequency GPS receivers.
