  This paper presents a simple and robust method for the automatic localisation
of static 3D objects in large-scale urban environments. By exploiting the
potential to merge a large volume of noisy but accurately localised 2D image
data, we achieve superior performance in terms of both robustness and accuracy
of the recovered 3D information. The method is based on a simple distributed
voting schema which can be fully distributed and parallelised to scale to
large-scale scenarios. To evaluate the method we collected city-scale data sets
from New York City and San Francisco consisting of almost 400k images spanning
the area of 40 km$^2$ and used it to accurately recover the 3D positions of
traffic lights. We demonstrate a robust performance and also show that the
solution improves in quality over time as the amount of data increases.
