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.
Original language | English (US) |
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Title of host publication | 2022 IEEE International Conference on Robotics and Automation, ICRA 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2244-2250 |
Number of pages | 7 |
ISBN (Electronic) | 9781728196817 |
DOIs | |
State | Published - 2022 |
Event | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States Duration: May 23 2022 → May 27 2022 |
Publication series
Name | 2022 International Conference on Robotics and Automation (ICRA) |
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Conference
Conference | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 |
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Country/Territory | United States |
City | Philadelphia |
Period | 5/23/22 → 5/27/22 |
Bibliographical note
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