This paper introduces the appliance of standardized, free to use and globally available geodata for autonomous robot navigation. For this, data from the famous collaborative OpenStreetMap (OSM) mapping project are used. These geodata are public domain and include rich information about streets, tracks, railways, waterways, points of interest, land use, building information and much more. Beyond the spatial information, the geodata contain detailed information about the name, type and width of the streets as well as public speed limits. As a contribution of this paper, the OSM data are integrated for the first time into the robot tasks of localization, path planning and autonomous vehicle control. Following the description of the approach, experimental results in outdoor environments demonstrate the effectiveness of this approach.

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This paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is applied to time-variant information about obstacles and driveable routes in the workspace of the autonomous robot and used for solving the navigation cycle of the robot. This includes localization and path planning as well as vehicle control. The presented approach is evaluated in a real-world experiment within changing indoor environment. The results show that the environmental representation is stable, improves its quality over time, and adapts to changes.

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