Improved GPS sensor model for mobile robots in urban terrain

Autonomous robot navigation in out-door scenarios gains increasing importance in various growing application areas. Whereas in non-urban domains such as deserts the problem of successful GPS-based navigation appears to be almost solved, navigation in urban domains particularly in the close vicinity of buildings is still a challenging problem. In such situations GPS accuracy significantly drops down due to multiple signal reflections with larger objects causing the so-called multipath error. In this paper we contribute a novel approach for incorporating multipath errors into the conventional GPS sensor model by analyzing environmental structures from online generated point clouds. The approach has been validated by experimental results conducted with an all-terrain robot operating in scenarios requiring close-to-building navigation. Presented results show that positioning accuracy can significantly be improved within urban domains.