Zhenyu Guo

About

Zhenyu Guo is a leading researcher in robotics and autonomous navigation, with a primary focus on simultaneous localization and mapping (SLAM) in dynamic environments and robotic plume source localization. His most impactful work, "DIG-SLAM: an accurate RGB-D SLAM based on instance segmentation and geometric clustering for dynamic indoor scenes" (2023, 26 citations), addresses a critical limitation of traditional visual SLAM systems that fail in environments with moving objects. By integrating instance segmentation and geometric clustering, Guo's approach enables robots to robustly navigate and map dynamic indoor scenes, significantly advancing real-world deployment of autonomous systems. Beyond SLAM, Guo has pioneered innovative strategies for autonomous plume source localization, including a finite state machine and YOLOv3-tiny-based system for near-source search (2023, 5 citations) and a multi-robot framework using Dirichlet Process Gaussian Mixture Models and mutation random salp swarm algorithms (2023, 2 citations). These contributions are vital for environmental monitoring, hazardous gas detection, and disaster response robotics. Guo's work demonstrates a consistent drive to solve fundamental challenges in perception and decision-making for autonomous systems operating in complex, unstructured environments.

Research Focus

Key Achievements

Top Papers

1. 1
   DIG-SLAM: an accurate RGB-D SLAM based on instance segmentation and geometric clustering for dynamic indoor scenes

   26 citations · 2023
2. 2
   Autonomous plume Near-Source search assisted by intermittent visible plume information using finite state Machine and YOLOv3-tiny

   5 citations · 2023
3. 3
   Self-coloring-Driven Plume Source Localization Strategy for Multiple Robots Using Dirichlet Process Gaussian Mixture Model and Mutation Random Salp Swarm Algorithm

   2 citations · 2023

Key Collaborators

Contact & Links