@conference {6696980,
	title = {Finding planes in LiDAR point clouds for real-time registration},
	booktitle = {Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on},
	year = {2013},
	month = {Nov},
	abstract = {<p>We present a robust plane finding algorithm that when combined with plane-based frame-to-frame registration gives accurate real-time pose estimation. Our plane extraction is capable of handling large and sparse datasets such as those generated from spinning multi-laser sensors such as the Velodyne HDL-32E LiDAR. We test our algorithm on frame-to-frame registration in a closed-loop indoor path comprising 827 successive 3D laser scans (over 57 million points), using no additional information (e.g., odometry, IMU). Our algorithm outperforms, in both accuracy and time, three state-of-the-art methods, based on iterative closest point (ICP), plane-based randomized Hough transform, and planar region growing.</p>},
	keywords = {3D laser scans, closed loop systems, closed-loop indoor path, distance measurement, Equations, Hough transform, Hough transforms, ICP, image registration, iterative closest point, Iterative closest point algorithm, iterative methods, Laser radar, Laser theory, LiDAR point clouds, Measurement by laser beam, odometry, optical radar, plane-based frame-to-frame registration, pose estimation, real-time pose estimation, real-time registration, real-time systems, robust plane finding, Sensors, sparse datasets, spinning multilaser sensors, Vectors, Velodyne HDL-32E LiDAR},
	doi = {10.1109/IROS.2013.6696980},
	author = {Grant, W.S. and Voorhies, R.C. and Laurent Itti}
}