Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments

Okechukwu Ihemadu, Wasif Naeem, Stuart Ferguson, Jing Deng

Research output: Contribution to conferencePaper

Abstract

This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.
Original languageEnglish
Pages282-287
Number of pages6
DOIs
Publication statusPublished - Jul 2013
EventIEEE 9th International Workshop on Robot Motion and Control. - Wasowo Palace, Poznan., Poland
Duration: 03 Jul 201305 Jul 2013

Conference

ConferenceIEEE 9th International Workshop on Robot Motion and Control.
CountryPoland
CityWasowo Palace, Poznan.
Period03/07/201305/07/2013

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    Ihemadu, O., Naeem, W., Ferguson, S., & Deng, J. (2013). Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments. 282-287. Paper presented at IEEE 9th International Workshop on Robot Motion and Control., Wasowo Palace, Poznan., Poland. https://doi.org/10.1109/RoMoCo.2013.6614622