Abundant image and sensory data collected over the last decades represents an invaluable source of information for cataloging and monitoring of the environment. Fusion of heterogeneous data sources is a challenging but promising tool to efficiently leverage such information. In this work we propose a pipeline for automatic detection and geolocation of recurring stationary objects deployed on fusion scenario of street level imagery and LiDAR point cloud data. The objects are geolocated coherently using a fusion procedure formalized as a Markov random field problem. This allows us to efficiently combine information from object segmentation, triangulation, monocular depth estimation and position matching with LiDAR data. The proposed fusion approach produces object mappings robust to scenes reporting multiple object instances. We introduce a new challenging dataset of over 200 traffic lights in Dublin city centre and demonstrate high performance of the proposed methodology and its capacity to perform multi-sensor data fusion.