Visual sorting robots often struggle with conventional horizontal bounding boxes when detecting irregularly placed solid waste. These boxes exhibit excessive background noise, making it difficult to extract precise depth data for 3D pose estimation. To address this issue, we developed MCB-Oriented RCNN, a more efficient detector for rotated objects based on the Oriented-RCNN framework. We replaced the resource-intensive ResNet-50 backbone with MobileNet-V3 to increase inference speed and integrated BiFPN and Coordinate Attention to maintain localization accuracy. Since lightweight models often suffer from accuracy losses, a teacher-student distillation strategy was applied to restore performance. Our experiments on a custom waste dataset show that the model achieves an mAP of 97.16% at 43.0 FPS. This corresponds to a 74.8% speed increase over the baseline model with a negligible accuracy loss of only 0.74%. These rotated boxes provide a direct approach to computing the 6-DoF coordinates required for robotic grasping.
Guanghong Tao, Haoyu Ma, Cheng-Gang Li et al.· 2026 IEEE International Conf...· 0 citations
The scalability of organic agriculture is partially limited by the labor costs associated with monitoring for pests. While drones and rovers are well-suited for agricultural monitoring from above or next to plants, many pests live on the underside of leaves or on plant stems, making them detectable only after they have caused significant damage. To enable early pest detection we present STEMbot, a miniature climbing robot system designed for autonomous navigation under plant canopies. Unlike existing climbing platforms that lack on-board perception or are restricted to unbranched vertical trunks, STEMbot integrates a fully geometric PIN-SLAM pipeline with a semantic OcTree to achieve robust localization and mapping while climbing the plant. To plan STEMbot's motion we propose a manifold-constrained A* planner along with ray-tracing goal specification to enable branch-aware traversal and the inspection of occluded targets. We validate our system through hardware experiments, demonstrating reliable traversal of stems ranging from 7-33mm and autonomous navigation across four distinct plant specimens. Quantitative evaluations show that our system achieves high-fidelity geometric reconstructions with an average Chamfer distance of less than 1cm relative to an offline photogrammetry baseline, confirming that STEMbot maintains the globally consistent odometry needed for autonomous navigation.
Zachary S. Charlick, N. R. Choudhury, Haoyu Ma et al.· 0 citations
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