三维激光扫描地图生成
8页1、Using Laser Range Data for 3D SLAM in Outdoor EnvironmentsDavid M. Cole and Paul M. Newman Oxford University Robotics Research Group Department of Engineering Science University of Oxford Parks Road, Oxford, OX1 3PJ, UK dmc, pnewmanrobots.ox.ac.ukAbstractTraditional Simultaneous Localization and Map- ping (SLAM) algorithms have been used to great effect inflat, indoor environments such as corridors and offices. We demonstrate that with a few augmentations, existing 2D SLAM technology can be exte
2、nded to perform full 3D SLAM in less benign, outdoor, undulating environments. In particular, we willuse data acquired with a 3D laser range finder. We use a simple segmentation algorithm to separate the data stream into distinct point clouds, each referenced to a vehicle position. The SLAM technique we then adopt inherits much from 2D Delayed State (or scan-matching) SLAM in that the state vector is an ever growing stack of past vehicle positions and inter-scan registrations are used to form me
3、asurements between them. The registration algorithm used is a novel combination of previous techniques carefully balancing the need for maximally wide convergence basins, robustness and speed. In addition, we introduce a novelpost-registration classification technique to detect matches which have converged to incorrect local minima.Index TermsMobile Robotics, Outdoor 3D SLAM, 3D Laser Data, Delayed State EKF, Point Cloud SegmentationI. INTRODUCTION ANDPREVIOUSWORKThe SLAM problem has been thorou
4、ghly researched theo- retically, and has been demonstrated many times on mobilerobots in flat, 2D environments. Some successful implemen-tations working in corridors and offices include those in 1 and 2. Recently, some researchers have looked towards performing SLAM on mobile robots in fully 3D, outdoor environments, including work in 3 and 4. Perhaps the most successful work to date has been in 5 and 6, which present very compelling results. However, unlike this work they do not use a probabili
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