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The role of Bayesian bounds in comparing SLAM algorithms performance

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Andrea CensiAuthors Info & Claims

PerMIS '08: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems

Pages 271 - 277

https://doi.org/10.1145/1774674.1774717

Published: 19 August 2008 Publication History

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Abstract

It is certainly hard to establish performance metrics for intelligent systems. Thankfully, no intelligence is needed to solve SLAM at all. Actually, when we cast the SLAM problem in the Bayesian framework, we already have a formula for the solution --- SLAM research is essentially about finding good approximations to this computationally monstrous formula. Still, SLAM algorithms are difficult to analyze formally, partly because of such out-of-model ad hoc approximations. This paper explains the role of Bayesian bounds in the analysis of such algorithms, according to the principle that sometimes it is better to analyze the problem than the solutions. The theme is explored with particular regard to the problem of comparing algorithms using different representations and different prior information.

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Ma YLi ZSotelo M(2020)Testing and Evaluating Driverless Vehicles' Intelligence: The Tsinghua Lion Case StudyIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2020.301443212:4(10-22)Online publication date: Dec-2021
https://doi.org/10.1109/MITS.2020.3014432
Ortiz SYu WZamora E(2018)Sliding Mode SLAM for Robust Simultaneous Localization and MappingIECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON.2018.8591121(5674-5679)Online publication date: Oct-2018
https://doi.org/10.1109/IECON.2018.8591121

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PerMIS '08: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems

August 2008

333 pages

ISBN:9781605582931

DOI:10.1145/1774674

Conference Chairs:
Raj Madhavan
Oak Ridge National Laboratory/NIST
,
Elena Messina
Intelligent Systems Division, NIST

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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Published: 19 August 2008

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PerMIS '08: Performance Metrics for Intelligent Systems

August 19 - 21, 2008

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Ma YLi ZSotelo M(2020)Testing and Evaluating Driverless Vehicles' Intelligence: The Tsinghua Lion Case StudyIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2020.301443212:4(10-22)Online publication date: Dec-2021
https://doi.org/10.1109/MITS.2020.3014432
Ortiz SYu WZamora E(2018)Sliding Mode SLAM for Robust Simultaneous Localization and MappingIECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON.2018.8591121(5674-5679)Online publication date: Oct-2018
https://doi.org/10.1109/IECON.2018.8591121

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Evaluation of SLAM Algorithms for Search and Rescue Applications

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction

SLAM and navigation in indoor environments

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