This work demonstrates how an autonomous robotic platform can use intrinsically noisy, coarse-scale visual methods lacking range information to produce good estimates of the location of objects, by using a map-space representation for weighting together multiple observations from different vantage points. As the robot moves through the environment it acquires visual images which are processed by means of a fast but noisy visual detection algorithm that gives bearing only information. The results from the detection are projected from image space into map space, where data from multiple viewpoints can intrinsically combine to yield an increasingly accurate picture of the location of objects. This method has been implemented and shown to work for object localization on a real robot. It has also been tested extensively in simulation, with systematically varied false positive and false negative detection rates. The results demonstrate that this is a viable method for object localization, even under a wide range of sensor uncertainties.