# Collaborative Localization for Micro Aerial Vehicles

## TL;DR

This paper introduces a decentralized collaborative localization framework for groups of MAVs using vision-based sensing, combining individual and relative pose estimation to improve accuracy in real and simulated environments.

## Contribution

It presents a novel distributed algorithm that integrates feature-based environment mapping with relative pose estimation among MAVs.

## Key findings

- Enhanced pose estimation accuracy through collaboration.
- Effective decentralized algorithm demonstrated in real and simulated flights.
- Robust feature matching and outlier rejection improve localization.

## Abstract

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.