Selection and Recognition of Landmarks Using Terrain Spatiograms

A team of robots working to explore and map an area may need to share information about landmarks so as to register their local maps and to plan effective exploration strategies. In previous papers we have introduced a combined image and spatial representation for landmarks: terrain spatiograms. We have shown that for manually selected views, terrain spatiograms provide an effective, shared representation that allows for occlusion filtering and a combination of multiple views. In this paper, we present a landmark saliency architecture (LSA) for automatically selecting candidate landmarks. Using a dataset of 21 outdoor stereo images generated by LSA, we show that the terrain spatiogram representation reliably recognizes automatically selected landmarks. The terrain spatiogram results are shown to improve on two purely appearance based approaches: template matching and image histogram matching

Selection and Recognition of Landmarks Using Terrain Spatiograms

A team of robots working to explore and map an area may need to share information about landmarks so as to register their local maps and to plan effective exploration strategies. In previous papers we have introduced a combined image and spatial representation for landmarks: terrain spatiograms. We have shown that for manually selected views, terrain spatiograms provide an effective, shared representation that allows for occlusion filtering and a combination of multiple views. In this paper, we present a landmark saliency architecture (LSA) for automatically selecting candidate landmarks. Using a dataset of 21 outdoor stereo images generated by LSA, we show that the terrain spatiogram representation reliably recognizes automatically selected landmarks. The terrain spatiogram results are shown to improve on two purely appearance based approaches: template matching and image histogram matching