Heterogeneous Deep Metric Learning for Ground and Aerial Point Cloud-Based Place Recognition

In this letter, we propose a heterogeneous deep metric learning pipeline for ground and aerial point cloud-based place recognition in large-scale environments. The pipeline extracts local features from ground and aerial raw point clouds by a sparse convolution module. The local features are processed by transformer encoders to capture the overlaps between ground and aerial point clouds, and then transformed to unified descriptors for retrieval purposes by backpropagation of heterogeneous loss functions. To facilitate training and provide a reliable benchmark, a large-scale dataset is also proposed, which is collected from well-equipped ground and aerial robotic platforms. We demonstrate the superiority of the proposed method by comparing it with existing well-performed methods. We also show that our method is capable to detect loop closures in a collaborative ground and aerial robotic system in the experimental results.