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In this paper, we present a vision based collaborative localization frameworkfor groups of micro aerial vehicles (MAV). The vehicles are each assumed to beequipped with a forward-facing monocular camera, and to be capable ofcommunicating with each other. This collaborative localization approach isbuilt upon a distributed algorithm where individual and relative poseestimation techniques are combined for the group to localize againstsurrounding environments. The MAVs initially detect and match salient featuresbetween each other to create a sparse reconstruction of the observedenvironment, which acts as a global map. Once a map is available, each MAVperforms feature detection and tracking with a robust outlier rejection processto estimate its own six degree-of-freedom pose. Occasionally, the MAVs can alsofuse relative measurements with individual measurements through featurematching and multiple-view geometry based relative pose computation. We presentthe implementation of this algorithm for MAVs and environments simulated withinMicrosoft AirSim, and discuss the results and the advantages of collaborativelocalization.

Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms