Landmark mapping from unbiased observations

The starting point of any Smart City approach is knowing what is in the city and the location of city assets. We propose a general, automated approach to inventorying and monitoring outdoor city infrastructure using common sensors: namely a GPS, IMU, and camera. The presented mapping algorithm operates in the mobile sensing paradigm, using observations from a moving vehicle to construct a map of landmark location estimates whose uncertainty decreases linearly with the number of observations, robust to both translational and angular error to first order. The algorithm is adaptable to many applications given an appropriate image classifier. We apply our algorithm to automatically locate and inventory city streetlights and demonstrate its performance using both numerical simulation and field experiments.