Abstract-Methods for acquisition and maintenance of an environment model are central to a broad class of mobility and navigation problems. Towards this end, various metric, topological or hybrid models have been proposed. Due to recent advances in sensing and recognition, acquisition of semantic models of the environments have gained increased interest in the community. In this work, we will demonstrate a capability of using weak semantic models of the environment to induce different topological models, capturing the spatial semantics of the environment at different levels. In the first stage of the model acquisition, we propose to compute semantic layout of the street scenes imagery by recognizing and segmenting buildings, roads, sky, cars and trees. Given such semantic layout, we propose an informative feature characterizing the layout and train a classifier to recognize street intersections in challenging urban inner city scenes. We also show how the evidence of different semantic concepts can induce useful topological representation of the environment, which can aid navigation and localization tasks. To demonstrate the approach, we carry out experiments on a challenging dataset of omnidirectional inner city street views and report the performance of both semantic segmentation and intersection classification. The problem of robot localization and mapping constitutes one of the basic capabilities of autonomous robotics systems and has attracted a lot of attention in the community. There have been a large variety of maps proposed ranging from metric, topological and hybrid maps. Lot of progress has been made in the Simultaneous Localization and Mapping (SLAM) approach for acquisition of metric models with a single reference frame. The topological models represent environments as graphs and differ in how the nodes and connections between them are defined [17] , [4] , [16] . An example of an on-line topological model acquisition by means of place recognition was tackled in [5] and was instrumental in solving the loop detection problem. Despite the progress in acquiring different types of representations of the environment, the connections between different types of models were explored in more limited settings. Examples of hybrid metric and topological maps have been introduced in indoors environments, where topology was induced by Voronoi tessellation of the occupancy map or polygonal model of the environment [1], [4] . Numerous approaches to topological mapping although providing the discrete representation of the environment, typically focus on the representation which would maximize the performance of loop detection [5] or place recognition tasks [18] without considerations of how well the topology captures the spatial layout of the environment.
Recently, Singh and Kosecka REF have proposed a semantic model for urban scene recognition segmenting buildings, road, sky, cars and trees.
Acquiring semantics induced topology in urban environments
2012 IEEE International Conference on Robotics and Automation
2012 IEEE International Conference on Robotics and Automation
Engineering Computer Science