Parameter Optimization for Loop Closure Detection in Closed Environments

Nils Rottmann, Ralf Bruder, Honghu Xue, Achim Schweikard, Elmar Rueckert

Abstract

Tuning parameters is crucial for the performance of localization and mapping algorithms. In general, the tuning of the parameters requires expert knowledge and is sensitive to information about the structure of the environment. In order to design truly autonomous systems the robot has to learn the parameters automatically. Therefore, we propose a parameter optimization approach for loop closure detection in closed environments which requires neither any prior information, e.g. robot model parameters, nor expert knowledge. It relies on several path traversals along the boundary line of the closed environment. We demonstrate the performance of our method in challenging real world scenarios with limited sensing capabilities. These scenarios are exemplary for a wide range of practical applications including lawn mowers and household robots.
Original languageEnglish
Number of pages8
Publication statusPublished - 12.11.2020
EventProceedings of International Conference on Intelligent Robots and Systems 2020 - Las Vegas, United States
Duration: 25.10.202029.10.2020

Conference

ConferenceProceedings of International Conference on Intelligent Robots and Systems 2020
Abbreviated titleIROS 2020
Country/TerritoryUnited States
CityLas Vegas
Period25.10.2029.10.20

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