Zhishang Zhang
Papers
2
Total Citations
20
H-Index
2
About
Zhishang Zhang is a robotics researcher specializing in simultaneous localization and mapping (SLAM) and autonomous navigation for humanoid robots operating in unknown and dynamic environments. His work focuses on integrating computer vision, feature recognition, and sensor fusion to enable robust real-time object classification and robot localization. Zhang’s most cited paper, “An improved multi-object classification algorithm for visual SLAM under dynamic environment” (2021, 12 citations), advances the ability of robots to identify and track multiple moving objects while mapping their surroundings—a critical challenge for real-world deployment. His earlier influential work (2017, 8 citations) introduced a novel method combining Harris-scale-invariant feature transform (SIFT) recognition with laser mapping within an extended Kalman filter SLAM framework. This approach allows humanoid robots to recognize target objects and localize them in real time using laser-provided position data, avoiding the computational burden of mapping every object in the environment. Together, these contributions address key bottlenecks in robotic perception and navigation, offering practical solutions for robots that must operate safely and efficiently in cluttered, changing spaces.
Research Focus
Key Achievements
Top Papers
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