Effective Heterogeneous Point Cloud-Based Place Recognition and Relative Localization for Ground and Aerial Vehicles

Abstract

Place recognition and relative localization are crucial for realizing the potential of collaboration in ground and aerial robot teams. Many existing works focus only on ground robots and are not well-suited for heterogeneous robot systems in large-scale environments. In this paper, we propose a novel pipeline based on BEV density image, combined with an enhanced data structure, for place recognition in air-ground robotic collaboration systems. An efficient height alignment algorithm is proposed for relative localization. Extensive experiments on various types of public datasets validate the efficacy of our method compared to other SOTA works. We also show that our method is capable to detect inter- and intra-robot loop closures in a ground and aerial multi-session SLAM system.