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- W3199197006 abstract "We study a semantic SLAM problem where a robot is tasked with autonomous weeding under the corn canopy. The goal is to detect corn stalks and localize them in a global coordinate frame. This is a challenging scenario for existing algorithms because there is very little space between the camera and the plants, and the camera motion is primarily restricted to be along the row. To overcome these challenges, we present a multi-camera system where a side camera (facing the plants) is used for detection, whereas front and back cameras are used for motion estimation. Next, we show how semantic features in the environment (corn stalks, ground, and crop planes) can be used to develop a robust semantic SLAM solution and present results from field trials performed throughout the growing season across various cornfields." @default.
- W3199197006 created "2021-09-27" @default.
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- W3199197006 date "2022-05-23" @default.
- W3199197006 modified "2023-09-25" @default.
- W3199197006 title "ROW-SLAM: Under-Canopy Cornfield Semantic SLAM" @default.
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- W3199197006 doi "https://doi.org/10.1109/icra46639.2022.9811745" @default.
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