Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms

TL;DR

This paper introduces a vision-based collaborative localization system for micro aerial vehicle swarms using monocular cameras, combining distributed algorithms, feature matching, and environment mapping for accurate self-localization.

Contribution

It presents a novel monocular vision-based framework for MAV swarms that integrates distributed pose estimation, environment mapping, and relative measurement fusion.

Findings

Successful implementation in Microsoft AirSim simulations

Enhanced localization accuracy through collaborative feature matching

Robust outlier rejection improves pose estimation reliability

Abstract

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.