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This article describes a method to develop an advanced navigation capability for the standard platform of the IMAV indoor competition: the Parrot AR.Drone. Our development is partlybased on simulation, which requires both a re-alistic sensor and motion model. This article de-scribes how a visual map of the indoor environ-ment can be made, including the effect of sensor noise. In addition, validation results for the mo-tion model are presented. On this basis, it shouldbe possible to learn elevation maps, optimal paths on this visual map and to autonomously avoid obstacles based on optical flow.

With the progress made in active exploration, the robots of the Joint Rescue Forces are capable of making deliberative decisions about the distribution of exploration locations over the team. Experiments have been done which include information exchange between team-members at rendez-vous points. Last year progress has been made with robots with advanced mobility, such as the Kenaf and the AirRobot. Currently our navigation algorithms are extended to be able to autonomously explore with the AirRobots. Robots equipped with both camera and laser-range scanners can learn a visual classifier of free space, which could be used by robots without laser-range scanners to navigate through the environment. Part of our algorithms have been validated on the Nomad Super Scout II robot available in our laboratory.