ROW-SLAM：トウモロコシ群落内のセマンティックSLAM
ROW-SLAM: Under-Canopy Cornfield Semantic SLAM
トウモロコシ群落内での除草ロボット向けに、側面カメラで茎を検出し前後カメラで自己位置推定を行うマルチカメラ構成のセマンティックSLAMを提案し、生育期を通じた圃場試験で有効性を示した。
著者: Jiacheng Yuan, Jungseok Hong, Junaed Sattar, Volkan Isler
分類: cs.RO
原文アブストラクト
We study a semantic SLAM problem faced by a robot 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 setup 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.
