NOctoSLAM: Fast Octree Surface Normal Mapping and Registration
Conference paper
Joscha Fossel, Karl Tuyls, Benjamin Schnieders, Daniel Claes, Daniel Hennes (September 2017)
cite
@inproceedings{fossel2017noctoslam,
title={{NOctoSLAM: Fast Octree Surface Normal Mapping and Registration}},
author={J. Fossel and K. Tuyls and B. Schnieders and D. Claes and D. Hennes},
booktitle={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2017},
pages={6764--6769},
doi={10.1109/IROS.2017.8206594}
}
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.
Continue reading...