A Novel Large-Scale Collaborative Mapping Framework with Heterogeneous Point Clouds for Aerial-Ground Robots

Abstract

Ground and aerial robots, with distinct sensing perspectives, acquire heterogeneous point clouds that exhibit limited overlap, presenting significant challenges for collaborative mapping. To address these challenges, this article proposes a robust LiDAR-based aerial-ground collaborative mapping framework for large-scale outdoor environments. Firstly, to perform reliable cross-source place recognition and detect loop closure between aerial-ground robots, a deep network that fuses multi-level bird’s-eye view (BEV) and geometric features is developed to ensure consistent feature extraction and emphasize overlaps between heterogeneous point clouds. Next, an overlap-aware registration method is proposed to align point clouds within a detected loop closure. This method can strategically perform point cloud sparsification based on overlap ratio estimation, and mitigate the adverse effects of interfering points in non-overlapping regions. Furthermore, a graph optimization is implemented to consider all loop closure constraints simultaneously and ensure global map consistency. Comparative experiments on public and self-collected datasets demonstrate the superiority of the proposed approach. We open source code on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> to benefit the community.