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Descripción
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| In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of the particle filter is performed using the 3D pose of the aerial robot estimated by the stereo VO algorithm. This predicted 3D pose is updated using inertial as well as semantic measurements. The algorithm processes semantic measurements in two phases; firstly, a pre-trained deep learning (DL) based object detector is used for real time object detections in the RGB spectrum. Secondly, from the corresponding 3D point clouds of the detected objects, we segment their dominant horizontal plane and estimate their relative position, also augmenting a prior map with new detections. The augmented map is then used in order to obtain a drift free pose estimate of the aerial robot | |
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Internacional
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Si |
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Nombre congreso
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2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
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Tipo de participación
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960 |
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Lugar del congreso
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Madrid |
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Revisores
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ISBN o ISSN
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2153-0858 |
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DOI
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Fecha inicio congreso
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01/10/2018 |
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Fecha fin congreso
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05/10/2018 |
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Desde la página
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1018 |
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Hasta la página
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1023 |
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Título de las actas
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- |