Meiqi Guo

Papers

Total Citations

H-Index

About

Meiqi Guo is a researcher advancing the field of robotic perception and manipulation, with a primary focus on object detection in dense, cluttered environments. Their most cited work, "Oriented bounding box detection algorithm for dense scenarios of robotic arm operation" (2025), introduces a novel approach to accurately localize objects in complex scenes where traditional axis-aligned bounding boxes fail. This contribution is critical for improving the precision and reliability of robotic arms in industrial and service applications, such as assembly, sorting, and autonomous grasping. With 3 citations to date, this paper has already garnered attention from peers working on computer vision and robotics integration. Guo’s research addresses a key bottleneck in real-world robotic systems—handling occlusions and overlapping objects—by leveraging oriented bounding boxes to better capture object orientation and spatial relationships. Their work stands out for its practical applicability, bridging the gap between theoretical detection algorithms and the demands of dynamic, dense operational settings. As a rising voice in robotics, Meiqi Guo continues to push the boundaries of how machines perceive and interact with their surroundings, making their research essential reading for students and engineers developing next-generation autonomous systems.