4 accepted papers
Autonomous exploration in large-scale and complex environments is a challenging task. As the size of the environment increases, the significant overhead of exploration algorithms could overwhelm the computational capability of mobile platforms, prohibiting timely response to environmental changes. M
Compared with using only a single type of robot, the use of drones and ground vehicles to jointly explore unknown areas can bring efficiency improvements. However, due to the difficulty of ground-aerial loop detection and especially the lack of ground-air datasets in large outdoor scenes, there is n
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 processe
The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications. This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning (RL) approach to develop a decentralized…