Urban Search and Rescue missions raise special\udrequirements on robotic systems. Small aerial systems provide\udessential support to human task forces in situation assessment and\udsurveillance. As external infrastructure for navigation and communication\udis usually not available, robotic systems must be able\udto operate autonomously. Limited payload of small aerial systems\udposes a great challenge to the system design. The optimal tradeoff\udbetween flight performance, sensors and computing resources\udhas to be found. Communication to external computers cannot be\udguaranteed, therefore all processing and decision making has to\udbe done on-board. In this paper, we present a UAS system design fulfilling these requirements. The components of our system are structured into groups to encapsulate their functionality and interfaces.We use both laser and stereo vision odometry to enable seamless indoor and outdoor navigation. The odometry is fused with an Inertial Measurement Unit in an Extended Kalman Filter. Navigation is supported by a module that recognizes known objects in the environment. A distributed computation approach is adopted to address computational requirements of the used algorithms. The capabilities of the system are validated in flight experiments, using a quadrotor.
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