research-article

Performance of 6D LuM and FFS SLAM: an example for comparison using grid and pose based evaluation methods

Authors:

Rolf Lakaemper,

Andreas Nüchter,

Longin Jan LateckiAuthors Info & Claims

PerMIS '07: Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems

Pages 250 - 256

https://doi.org/10.1145/1660877.1660913

Published: 28 August 2007 Publication History

Abstract

The focus of this paper is on the performance comparison of two simultaneous localization and mapping (SLAM) algorithms namely 6D Lu/Milios SLAM and Force Field Simulation (FFS). The two algorithms are applied to a 2D data set. Although the algorithms generate overall visually comparable results, they show strengths & weaknesses in different regions of the generated global maps. The question we address in this paper is, if different ways of evaluating the performance of SLAM algorithms project different strengths and how can the evaluations be useful in selecting an algorithm. We will compare the performance of the algorithms in different ways, using grid and pose based quality measures.

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Cited By

Lakaemper RAdluru N(2018)Using virtual scans for improved mapping and evaluationAutonomous Robots10.1007/s10514-009-9149-427:4(431-448)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10514-009-9149-4
Lakaemper RMadhavan RMessina EMadhavan R(2010)Towards evaluating world modeling for autonomous navigation in unstructured and dynamic environmentsProceedings of the 10th Performance Metrics for Intelligent Systems Workshop10.1145/2377576.2377640(355-360)Online publication date: 28-Sep-2010
https://dl.acm.org/doi/10.1145/2377576.2377640
Lakaemper RAdluru NMadhavan RMessina E(2008)Using virtual scans to improve alignment performance in robot mappingProceedings of the 8th Workshop on Performance Metrics for Intelligent Systems10.1145/1774674.1774716(265-270)Online publication date: 19-Aug-2008
https://dl.acm.org/doi/10.1145/1774674.1774716

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cover image ACM Other conferences

PerMIS '07: Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems

August 2007

293 pages

ISBN:9781595938541

DOI:10.1145/1660877

General Chair:
Elena Messina
Intelligent Systems Division, NIST
,
Program Chair:
Raj Madhavan
Oak Ridge National Laboratory/NIST

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NIST: National Institute of Standards and Technology

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

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Published: 28 August 2007

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PerMIS07: Performance Metrics for Intelligent Systems 2007

August 28 - 30, 2007

D.C., Washington

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Cited By

Lakaemper RAdluru N(2018)Using virtual scans for improved mapping and evaluationAutonomous Robots10.1007/s10514-009-9149-427:4(431-448)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10514-009-9149-4
Lakaemper RMadhavan RMessina EMadhavan R(2010)Towards evaluating world modeling for autonomous navigation in unstructured and dynamic environmentsProceedings of the 10th Performance Metrics for Intelligent Systems Workshop10.1145/2377576.2377640(355-360)Online publication date: 28-Sep-2010
https://dl.acm.org/doi/10.1145/2377576.2377640
Lakaemper RAdluru NMadhavan RMessina E(2008)Using virtual scans to improve alignment performance in robot mappingProceedings of the 8th Workshop on Performance Metrics for Intelligent Systems10.1145/1774674.1774716(265-270)Online publication date: 19-Aug-2008
https://dl.acm.org/doi/10.1145/1774674.1774716

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