Hang Meng
Papers
1
Total Citations
21
H-Index
1
About
Hang Meng is a researcher at the forefront of service robotics and autonomous navigation, with a particular focus on enabling robots to operate seamlessly in multi-story environments. His key contributions lie in developing robust perception and localization systems that allow robots to interact with human-centric infrastructure, such as elevators, without the need for specialized hardware. Meng’s most cited work, “Automatic Elevator Button Localization Using a Combined Detecting and Tracking Framework for Multi-Story Navigation” (2020, 21 citations), addresses a critical gap in simultaneous localization and mapping (SLAM) by proposing a vision-based framework that detects and tracks elevator buttons in real time. This innovation is pivotal for service robots that must autonomously navigate between floors in modern buildings, a task previously reliant on costly hardware modifications. By integrating detection and tracking, Meng’s approach enhances robustness under varying lighting and button conditions, directly advancing the practicality of indoor robot deployment. His research bridges computer vision and robotics, offering scalable solutions for smart building automation. With a growing citation footprint, Meng’s work is increasingly recognized as foundational for next-generation autonomous systems that must operate in human-designed spaces.
Research Focus
Key Achievements
Top Papers
- 1