  In this paper, we present a framework for performing collaborative
localization for groups of micro aerial vehicles (MAV) that use vision based
sensing. The vehicles are each assumed to be equipped with a forward-facing
monocular camera, and to be capable of communicating with each other. This
collaborative localization approach is developed as a decentralized algorithm
and built in a distributed fashion where individual and relative pose
estimation techniques are combined for the group to localize against
surrounding environments. The MAVs initially detect and match salient features
between each other to create a sparse reconstruction of the observed
environment, which acts as a global map. Once a map is available, each MAV
performs feature detection and tracking with a robust outlier rejection process
to estimate its own pose in 6 degrees of freedom. Occasionally, one or more
MAVs can be tasked to compute poses for another MAV through relative
measurements, which is achieved by exploiting multiple view geometry concepts.
These relative measurements are then fused with individual measurements in a
consistent fashion. We present the results of the algorithm on image data from
MAV flights both in simulation and real life, and discuss the advantages of
collaborative localization in improving pose estimation accuracy.
