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Sound source localization on a mobile robot can be a difficult task due to a variety of problems inherent to a real environment, including robot ego-noise, echoes, and the transient nature of ambient noise. As a result, source localization data are often very noisy and unreliable. In this work, we overcome some of these problems by combining the localization evidence over a variety of robot poses using an evidence grid. The result is a representation that localizes the pertinent objects well over time, can be used to filter poor localization results, and may also be useful for global re-localization from sound localization results