<title>Multiple-platform localization and map building</title>

Abstract

This paper presents current work on decentralized data fusion (DDF) applied to multiple unmanned aerial vehicles. The benefits of decentralizing algorithms, particularly in this field, are enormous. At a mission level, multiple aircraft may fly together sharing information with one another in order to produce more accurate and coherent estimates, and hence increase the chances of success. At the single platform level, algorithms may be decentralized throughout the airframe reducing the probability of catastrophic failure by eliminating the dependency on a particular central processing facility. To this end, a complex simulator has been developed to test and evaluate decentralized picture compilation, platform localization and simultaneous localization and map building (SLAM) algorithms which are to be implemented on multiple airborne vehicles. This simulator is both comprehensive and modular, enabling multiple platforms carrying multiple distributed sensors to be modeled and interchanged easily. The map building and navigation algorithms interface with both the simulator and the real airframe in exactly the same way in order to evaluate the actual flight code as comprehensively as possible. Logged flight data can also be played back through the simulator to the navigation routines instead of simulated sensors. This paper presents the structure of both the simulator and the algorithms that have been developed. An example of decentralized map building is included, and future work in decentralized navigation and SLAM systems is discussed.