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Benchmarking urban six‐degree‐of‐freedom simultaneous localization and mapping
Author(s) -
Wulf Oliver,
Nüchter Andreas,
Hertzberg Joachim,
Wagner Bernardo
Publication year - 2008
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20234
Subject(s) - benchmarking , simultaneous localization and mapping , ground truth , computer science , artificial intelligence , degree (music) , computer vision , data mining , mobile robot , robot , physics , marketing , acoustics , business
Abstract Quite a number of approaches for solving the simultaneous localization and mapping (SLAM) problem exist by now. Some of them have recently been extended to mapping environments with six‐degree‐of‐freedom poses, yielding 6D SLAM approaches. To demonstrate the capabilities of the respective algorithms, it is common practice to present generated maps and successful loop closings in large outdoor environments. Unfortunately, it is nontrivial to compare different 6D SLAM approaches objectively, because ground truth data about the outdoor environments used for demonstration are typically unavailable. We present a novel benchmarking method for generating the ground truth data based on reference maps. The method is then demonstrated by comparing the absolute performance of some previously existing 6D SLAM algorithms that build a large urban outdoor map. © 2008 Wiley Periodicals, Inc.