Optimizing the efficiency of the sub-map technique for large-scale simultaneous localization and mapping

Okechukwu Ihemadu, Wasif Naeem, Stuart Ferguson

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

A technique for optimizing the efficiency of the sub-map method for large-scale simultaneous localization and mapping (SLAM) is proposed. It optimizes the benefits of the sub-map technique to improve the accuracy and consistency of an extended Kalman filter (EKF)-based SLAM. Error models were developed and engaged to investigate some of the outstanding issues in employing the sub-map technique in SLAM. Such issues include the size (distance) of an optimal sub-map, the acceptable error effect caused by the process noise covariance on the predictions and estimations made within a sub-map, when to terminate an existing sub-map and start a new one and the magnitude of the process noise covariance that could produce such an effect. Numerical results obtained from the study and an error-correcting process were engaged to optimize the accuracy and convergence of the Invariant Information Local Sub-map Filter previously proposed. Applying this technique to the EKF-based SLAM algorithm (a) reduces the computational burden of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. A Monte Carlo analysis of the system is presented as a means of demonstrating the consistency and efficacy of the proposed technique.
Original languageEnglish
Pages (from-to)329-344
Number of pages16
JournalTransactions of the Institute of Measurement and Control
Volume37
Issue number3
Early online date28 Nov 2014
DOIs
Publication statusPublished - Mar 2015

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