Local Multi-resolution Surfel Grids for MAV Motion Estimation and 3D Mapping

For autonomous navigation in restricted environments, micro aerial vehicles (MAV) need to create 3D maps of their surroundings and must track their motion within these maps. In this paper, we propose an approach to simultaneous localization and mapping that is based on the measurements of a lightweight 3D laser-range finder. We aggregate laser-range measurements by registering sparse 3D scans with a local multiresolution surfel map that has high resolution in the vicinity of the MAV and coarser resolutions with increasing distance, which corresponds well to measurement density and accuracy of our sensor. Modeling measurement distributions within voxels by surface elements allows for efficient and accurate registration of 3D scans with the local map. The incrementally built local dense 3D maps of nearby key poses are registered globally by graph optimization. This yields a globally consistent dense 3D map of the environment. Continuous registration of local maps with the global map allows for tracking the 6D MAV pose in real time. In experiments, we demonstrate accuracy and efficiency of our approach.