Online three-dimensional SLAM by registration of large planar surface segments and closed-form pose-graph relaxation
Article first published online: 15 OCT 2009
Copyright © 2009 Wiley Periodicals, Inc.
Journal of Field Robotics
Special Issue: Three-Dimensional Mapping, Part 3
Volume 27, Issue 1, pages 52–84, January/February 2010
How to Cite
Pathak, K., Birk, A., Vaskevicius, N., Pfingsthorn, M., Schwertfeger, S. and Poppinga, J. (2010), Online three-dimensional SLAM by registration of large planar surface segments and closed-form pose-graph relaxation. J. Field Robotics, 27: 52–84. doi: 10.1002/rob.20322
- Issue published online: 3 DEC 2009
- Article first published online: 15 OCT 2009
- Manuscript Accepted: 17 AUG 2009
- Manuscript Received: 5 JAN 2009
A fast pose-graph relaxation technique is presented for enhancing the consistency of three-dimensional (3D) maps created by registering large planar surface patches. The surface patches are extracted from point clouds sampled from a 3D range sensor. The plane-based registration method offers an alternative to the state-of-the-art algorithms and provides advantages in terms of robustness, speed, and storage. One of its features is that it results in an accurate determination of rotation, although a lack of predominant surfaces in certain directions may result in translational uncertainty in those directions. Hence, a loop-closing and relaxation problem is formulated that gains significant speed by relaxing only the translational errors and utilizes the full-translation covariance determined during pairwise registration. This leads to a fast 3D simultaneous localization and mapping suited for online operations. The approach is tested in two disaster scenarios that were mapped at the NIST 2008 Response Robot Evaluation Exercise in Disaster City, Texas. The two data sets from a collapsed car park and a flooding disaster consist of 26 and 70 3D scans, respectively. The results of these experiments show that our approach can generate 3D maps without motion estimates by odometry and that it outperforms iterative closest point–based mapping with respect to speed and robustness. © 2009 Wiley Periodicals, Inc.