Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/28947
DC FieldValueLanguage
dc.contributor.authorHadjiloizou, Loizos-
dc.contributor.authorMakridis, Evagoras-
dc.contributor.authorCharalambous, Themistoklis-
dc.contributor.authorDeliparaschos, Kyriakos M.-
dc.date.accessioned2023-03-31T08:33:24Z-
dc.date.available2023-03-31T08:33:24Z-
dc.date.issued2023-06-
dc.identifier.citation31st Mediterranean Conference on Control and Automation, 26-29 June, Limassol, Cyprusen_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/28947-
dc.description.abstractWe present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.subjecta Maximum Correntropy Criterion Kalman Filteren_US
dc.subjectMultisensor fusion frameworken_US
dc.titleMaximum Correntropy Criterion Kalman Filter For Indoor Quadrotor Navigation Under Intermittent Measurementsen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationKTH Royal Institute of Technologyen_US
dc.collaborationUniversity of Cyprusen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryCyprusen_US
dc.countrySwedenen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceMediterranean Conference on Control & Automation (MED)en_US
dc.identifier.urlhttp://arxiv.org/abs/2303.09561v1-
cut.common.academicyear2022-2023en_US
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.fulltextWith Fulltext-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-0618-5846-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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