Adaptive path planning for UAVs for multi-resolution semantic segmentation
نویسندگان
چکیده
Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, usage of unmanned aerial vehicles (UAVs) for monitoring remote sensing is rapidly gaining momentum due to their high mobility, low cost, flexible deployment. A key challenge planning missions maximize value acquired large environments given flight time limitations. This is, example, relevant agricultural fields. paper addresses problem adaptive path accurate semantic segmentation using UAVs. We propose an online algorithm which adapts UAV paths obtain high-resolution segmentations necessary areas with fine details as they are detected incoming images. enables perform close inspections at altitudes only where required, without wasting energy on exhaustive mapping maximum image resolution. feature our approach new accuracy model deep learning-based architectures that captures relationship between altitude accuracy. evaluate different domains real-world data, proving efficacy generability solution. • UAVs scenarios. Our decision function allows dynamically changing focus interest. Experiments demonstrate generality
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ژورنال
عنوان ژورنال: Robotics and Autonomous Systems
سال: 2023
ISSN: ['0921-8890', '1872-793X']
DOI: https://doi.org/10.1016/j.robot.2022.104288