Fast registration based on noisy planes with unknown correspondences for 3-D mapping
IEEE Transactions on Robotics
Spectral registration of noisy sonar data for underwater 3D mapping
Autonomous Robots
Intelligent Service Robotics
Robotics and Autonomous Systems
Visual SLAM Based on Rigid-Body 3D Landmarks
Journal of Intelligent and Robotic Systems
Three-dimensional SLAM for mapping planetary work site environments
Journal of Field Robotics
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
Robotics and Autonomous Systems
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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.