In this paper, we address the problem of fusing information about object positions in multirobot systems. Our approach is novel in two main respects. First, it addresses the multirobot object localization problem using fuzzy logic. It uses fuzzy sets to represent uncertain position information and fuzzy intersection to fuse this information. The result of this fusion is a consensus among sources, as opposed to the compromise achieved by many other approaches. Second, our method fully propagates self-localization uncertainty to object-position estimates. We evaluate our method using systematic experiments, which describe an input-error landscape for the performance of our approach. This landscape characterizes how well our method performs when faced with various types and amounts of input errors.