NOctoSLAM: Fast octree surface normal mapping and registration

Abstract

In this paper, we introduce a SLAM front end called NOctoSLAM. The approach adopts an octree-based map representation that implicitly enables source and reference data association for point to plane ICP registration. Additionally, the data structure is used to group map points to approximate surface normals. The multi-resolution capability of octrees, achieved by aggregating information in parent nodes, enables us to compensate for spatially unbalanced sensor data typically provided by multi-line lidar sensors. The octree-based data association is only approximate, but our empirical evaluation shows that NOctoSLAM achieves the same pose estimation accuracy as a comparable, point cloud based approach. However, NOctoSLAM can perform twice as many registration iterations per time unit. In contrast to point cloud based surface normal maps, where the map update duration depends on the current map size, we achieve a constant map update duration including surface normal recalculation. Therefore, NOctoSLAM does not require elaborate and environment dependent data filters. The results of our experiments show a mean positional error of 0.029 m and 0.019 rad, with a low standard deviation of 0.005 m and 0.006 rad, outperforming the state-of-the-art by remaining accurate while running online.