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- W4313854513 abstract "Remote sensing methodologies are useful for managing natural systems. Among them, we focused on a methodology for collecting data by attaching three-dimensional (3D) light detection and ranging (LiDAR) sensors to unmanned aerial vehicles (UAVs) that can perform detection over a wide area with high mobility. In this study, we propose a semantic simultaneous localization and mapping (SLAM)-based autonomous driving system for tributary mapping by using a UAV equipped with a 3D LiDAR. As a natural system, tributaries are not studied extensively for diagnosing polluted watersheds and managing water quality. Therefore, it is necessary to develop a robust autonomous driving system for mapping tributary environments." @default.
- W4313854513 created "2023-01-10" @default.
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- W4313854513 date "2022-11-27" @default.
- W4313854513 modified "2023-09-30" @default.
- W4313854513 title "Semantic SLAM-based Autonomous Tributary Navigation System Using 3D LiDAR Point Cloud for UAV" @default.
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- W4313854513 doi "https://doi.org/10.23919/iccas55662.2022.10003939" @default.
- W4313854513 hasPublicationYear "2022" @default.
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