Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms
This addresses localization challenges for MAV swarms in GPS-denied environments, but it appears incremental as it builds on existing distributed algorithms and vision-based techniques.
The paper tackles collaborative localization for micro aerial vehicle swarms using only monocular cameras, combining individual and relative pose estimation techniques to create a global map from sparse reconstructions. Results show advantages in simulated environments using Microsoft AirSim, though no concrete performance numbers are provided.
In this paper, we present a vision based collaborative localization framework for groups of micro aerial vehicles (MAV). 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 built upon a distributed algorithm 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 six degree-of-freedom pose. Occasionally, the MAVs can also fuse relative measurements with individual measurements through feature matching and multiple-view geometry based relative pose computation. We present the implementation of this algorithm for MAVs and environments simulated within Microsoft AirSim, and discuss the results and the advantages of collaborative localization.