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- W1535841975 abstract "Mimicking human navigation is a challenging goal for autonomous robots. This requires to explicitly take into account not only geometric representation but also high-level interpretation of the environment. In this paper, we demonstrate the capability to infer a route in a global map by using semantics. Our approach relies on an object-based representation of the world automatically built by robots from spherical images. In addition, we propose a new approach to specify paths in terms of high-level robot actions. This path description provides robots with the ability to interact with humans in an intuitive way. We perform experiments on simulated and real-world data, demonstrating the ability of our approach to deal with complex large-scale outdoor environments whilst dealing with labelling errors." @default.
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- W1535841975 date "2015-05-01" @default.
- W1535841975 modified "2023-09-23" @default.
- W1535841975 title "Semantic representation for navigation in large-scale environments" @default.
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- W1535841975 doi "https://doi.org/10.1109/icra.2015.7139314" @default.
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