Active 3D Mapping Leveraging Heterogeneous Crowd Robot based-on Reinforcement Learning

The paper demonstrates that the introduction of heterogeneous crowd robots improves the accuracy and completeness of indoor 3D mapping. First, three ground robots (sweeping robot, inspection robot, and guidance robot) are used to perform active exploration and mapping tasks in the iGibson indoor environment. However, the mapping results of the ground robot reveal that there are obvious blind spots in key areas (such as tables, stoves, and beds), resulting in the lack of point cloud data. To overcome this challenge, we introduce a drone for active 3D mapping using its bird’s eye view and powerful perception capabilities. Experimental results show that by introducing drones, we have successfully eliminated the blind areas of vision existing in-ground robot mapping and achieved more comprehensive and accurate mapping results. This demonstration fully demonstrates the advantages of introducing heterogeneous crowd robots, and how the complementary capabilities of different types of robots can work together to improve the indoor 3D mapping process.