Collaborative Localization of Aerial and Ground Mobile Robots through Orthomosaic Map

TL;DR

This paper introduces a cooperative SLAM approach that enables an aerial drone and ground robot to accurately match and localize within a shared map despite large viewpoint differences, using dense mapping, template matching, and particle filtering.

Contribution

It presents a novel map matching and localization method for aerial-ground cooperative SLAM that handles large scale variance and sensor heterogeneity.

Findings

High accuracy in map matching demonstrated on Aero-Ground Dataset

Robustness to sensor types and synchronization issues

Fast and reliable localization performance

Abstract

With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems that have large scale variance of viewpoints of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software "Metashape", map matching with a proposed template matching algorithm, weighted normalized cross-correlation (WNCC) and localization with particle filter. The approach enables map matching for cooperative SLAM with the feasibility of multiple scene sensors, varies from stereo cameras to lidars, and is insensitive to the synchronization of the two systems. We demonstrate the accuracy, robustness, and the speed of the approach under experiments of the Aero-Ground Dataset.