近似数据关联的6D SLAM

ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005. Pub Date : 2005-07-18 DOI:10.1109/ICAR.2005.1507419

A. Nüchter, K. Lingemann, J. Hertzberg, H. Surmann

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引用次数: 165

摘要

本文提出了一种新的六自由度同步定位与制图方法。迭代最近点(ICP)算法的一种快速变体将移动机器人进行的3D扫描注册到一个公共坐标系中，从而提供重新定位。因此，数据关联被简化为寻找最近点的问题。本文提出并评价了这种搜索的近似算法，即近似kd树和盒分解树。基于三维扫描匹配，建立了一个考虑机器人姿态中所有自由参数的6D SLAM解决方案

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6D SLAM with approximate data association

This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. A fast variant of the iterative closest points (ICP) algorithm registers 3D scans taken by a mobile robot into a common coordinate system and thus provides relocalization. Hereby, data association is reduced to the problem of searching for closest points. Approximation algorithms for this searching, namely, approximate kd-trees and box decomposition trees, are presented and evaluated in this paper. A solution to 6D SLAM that considers all free parameters in the robot pose is built based on 3D scan matching

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ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.

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