  Autonomous vehicles and driver assistance systems utilize maps of 3D semantic
landmarks for improved decision making. However, scaling the mapping process as
well as regularly updating such maps come with a huge cost. Crowdsourced
mapping of these landmarks such as traffic sign positions provides an appealing
alternative. The state-of-the-art approaches to crowdsourced mapping use ground
truth camera parameters, which may not always be known or may change over time.
In this work, we demonstrate an approach to computing 3D traffic sign positions
without knowing the camera focal lengths, principal point, and distortion
coefficients a priori. We validate our proposed approach on a public dataset of
traffic signs in KITTI. Using only a monocular color camera and GPS, we achieve
an average single journey relative and absolute positioning accuracy of 0.26 m
and 1.38 m, respectively.
