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-theart 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. C

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This article addresses fast 3D mapping by a mobile robot in a predominantly planar environment. It is based on a novel pose registration algorithm based entirely on matching features composed of plane-segments extracted from point-clouds sampled from a 3D sensor. The approach has advantages in terms of robustness, speed and storage as compared to the voxel based approaches. Unlike previous approaches, the uncertainty in plane parameters is utilized to compute the uncertainty in the pose computed by scan-registration. The algorithm is illustrated by creating a full 3D model of a multi-level robot testing arena.

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