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This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse.
Terrain variations can drastically affect mobile robot navigation, but this new adaptive control architecture ensures stable movement across diverse greenhouse surfaces.
A low-cost RGB-D perception framework can now enable reliable autonomous navigation for agricultural robots in complex greenhouse environments, outperforming existing methods by up to 19% in mIoU.
Standardized benchmarks for agricultural robots are here: a ROS2 framework lets you rigorously compare control strategies under realistic greenhouse conditions, complete with physics simulation and disturbance modeling.
