State-of-the-art methods have recently achieved impressive performance for recognising the objects present in large databases of pre-collected images. There has been much less focus on building embodied systems that recognise objects present in the real world. This paper describes an intelligent system that attempts to perform robust object recognition in a realistic scenario, where a mobile robot moving through an environment must use the images collected from its camera directly to recognise objects. To perform successful recognition in this scenario, we have chosen a combination of techniques including a peripheral-foveal vision system, an attention system combining bottom-up visual saliency with structure from stereo, and a localisation and mapping technique. The result is a highly capable object recognition system that can be easily trained to locate the objects of interest in an environment, and subsequently build a spatial-semantic map of the region. This capability has been demonstrated during the Semantic Robot Vision Challenge, and is further illustrated with a demonstration of semantic mapping. We also empirically verify that the attention system outperforms an undirected approach even with a significantly lower number of foveations.