Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima nuclear reactor explosion, an unmanned helicopter capable of obtaining overhead images, gathering radiation measurements, and mapping both the structural and radiation content of the environment would have given the response team invaluable data early in the disaster, thereby allowing them to understand the extent of the damage and areas where dangers to personnel existed. With this motivation, the Unmanned Systems Lab at Virginia Tech has developed a remote sensing system for radiation detection and aerial imaging using a 90 kg autonomous helicopter and sensing payloads for the radiation detection and imaging operations. The radiation payload, which is the sensor of focus in this paper, consists of a scintillating type detector with associated software and novel search algorithms to rapidly and effectively map and locate sources of high radiation intensity. By incorporating this sensing technology into an unmanned aerial vehicle system, crucial situational awareness can be gathered about a post-disaster environment and response efforts can be expedited. This paper details the radiation mapping and localization capabilities of this system as well as the testing of the various search algorithms using simulated radiation data. The various components of the system have been flight tested over a several-year period and a new production flight platform has been built to enhance reliability and maintainability. The new system is based on the Aeroscout B1-100 helicopter Remote Sens. 2012, 4 1996 platform, which has a one-hour flight endurance and uses a COFDM radio system that gives the helicopter an effective range of 7 km.

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Aerial terrain mapping has been used for many years to monitor natural habitats and ecosystems, assist in urban planning, and monitor trends in land usage. Recent improvements in digital imaging, LiDAR, and synthetic aperture radar have facilitated the generation of 3-D terrain models for analysis in these applications. Unfortunately,thesesystemstypicallyrequirelargemannedaircraftandsignificant post-processing of data before viewable results are produced. This inhibits use of these technologies in time-critical applications such as disaster relief, autonomous obstacle avoidance, and landing-zone assessment for a vertical take-off and landing aircraft. This paper describes a wide-baseline stereo vision system that enables near-real-time generation of dense 3-D terrain maps. The key advantage of computational stereo vision over monocular structure-from-motion is that terrain can be reconstructed from a single synchronized pair of calibrated images. The paper describes a working prototype, and presents a novel approach for combining separate stereo maps into larger terrain mosaics. The new stereo system and algorithm have an accuracy rangingfrom56cmto65cmacrossthefieldofviewatanaltitudeof40m.Also, dense correlation of the imagery generates over 2200 points/m 2 . The system weighs just 3.1 kg, roughly one-fourth the weight of comparable high-altitude mapping systems, at ca. one-tenth the cost. The paper also describes potential implementations usingField-ProgrammableGateArrays(FPGAs)andApplication-SpecificIntegrated Circuits (ASICs) for real-time operation.