3D mapping for multi hybrid robot cooperation
It addresses mapping challenges for rescue robots, but appears incremental as it builds on existing registration and fusion techniques.
This paper tackles the problem of building consistent 3D maps for multi-robot cooperation in USAR environments by fusing sensor data from UAVs and UGVs, resulting in a globally optimized localization approach that demonstrates performance in two examples.
This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart from the structural information of the point clouds, it is important to mention that no further information is required for the localization. Two examples show the performance of the overall registration.