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dc.contributor.authorMathur, Pranay
dc.contributor.authorKhedekar, Nikhil Vijay
dc.contributor.authorAlexis, Konstantinos
dc.date.accessioned2022-04-01T11:46:19Z
dc.date.available2022-04-01T11:46:19Z
dc.date.created2022-01-17T16:57:24Z
dc.date.issued2021
dc.identifier.citation2021 20th International Conference on Advanced Robotics (ICAR)en_US
dc.identifier.isbn978-1-6654-3684-7
dc.identifier.urihttps://hdl.handle.net/11250/2989278
dc.description.abstractIn this paper, a computational resources-aware parameter adaptation method for visual-inertial navigation systems is proposed with the goal of enabling the improved deployment of such algorithms on computationally constrained systems. Such a capacity can prove critical when employed on ultra-lightweight systems or alongside mission critical computationally expensive processes. To achieve this objective, the algorithm proposes selected changes in the vision frontend and optimization back-end of visual-inertial odometry algorithms, both prior to execution and in real-time based on an online profiling of available resources. The method also utilizes information from the motion dynamics experienced by the system to manipulate parameters online. The general policy is demonstrated on three established algorithms, namely S-MSCKF, VINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset. The proposed approach achieved comparable performance at a fraction of the original computational cost.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof20th International Conference on Advanced Robotics (ICAR)
dc.titleResource-aware Online Parameter Adaptation for Computationally -constrained Visual-Inertial Navigation Systemsen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.pagenumber842-848en_US
dc.identifier.doi10.1109/ICAR53236.2021.9659338
dc.identifier.cristin1982935
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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