6D SLAM with approximate data association

Author

Nüchter, Andreas ; Lingemann, Kai ; Hertzberg, Joachim ; Surmann, Hartmut

Author_Institution

Inst. for Comput. Sci., Osnabruck Univ.

fYear

2005

fDate

18-20 July 2005

Firstpage

242

Lastpage

249

Abstract

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

Keywords

approximation theory; data analysis; iterative methods; mobile robots; tree searching; 3D scan matching; 6D SLAM; approximate data association; approximate kd-trees; approximation algorithm; box decomposition trees; coordinate system; iterative closest points algorithm; mapping problem; mobile robot; simultaneous localization; Approximation algorithms; Cloud computing; Iterative algorithms; Iterative closest point algorithm; Laser modes; Mobile robots; Robot kinematics; Robot sensing systems; Robotics and automation; Simultaneous localization and mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Seattle, WA

Print_ISBN

0-7803-9178-0

Type

conf

DOI

10.1109/ICAR.2005.1507419

Filename

1507419