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- W4221106736 abstract "Owing to its vast applicability, the semantic interpretation of regions or entities is increasingly attracting the attention of scholars in the robotics community. Recent research in robot vision has equipped, modern autonomous systems with the ability to semantically recognize and segment entities from scenes with the aim to effectively interpret the environment. Extending this notion, the semantic representation of the surroundings is considered to be a fundamental property for robot self-localization, especially in the absence of any georeferencing signal. In this paper, we present a robust algorithm to locate the position of an autonomous agent within a georeferenced map through particle filtering. Specifically, the proposed approach consists of (i) a motion model of metric data from visual odometry, (ii) an observation model of graph-based descriptors with semantic and metric information and (iii) a re-sampling model, based on the stochastic universal sampling. The above components are evaluated under an extensive set of experiments revealing the robustness and accuracy of our final self-localization system." @default.
- W4221106736 created "2022-04-03" @default.
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- W4221106736 date "2022-05-01" @default.
- W4221106736 modified "2023-10-14" @default.
- W4221106736 title "Self-localization based on terrestrial and satellite semantics" @default.
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- W4221106736 doi "https://doi.org/10.1016/j.engappai.2022.104824" @default.
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