Yajing Zou
Papers
2
Total Citations
30
H-Index
2
About
Yajing Zou is a leading researcher in autonomous robotics and perception systems, with a focus on simultaneous localization and mapping (SLAM) and visual-inertial pose estimation for mobile robots operating in unknown indoor environments. Their most-cited work, "Autonomous Exploration of Unknown Indoor Environments for High-Quality Mapping Using Feature-Based RGB-D SLAM" (2022, 17 citations), introduces a pioneering SLAM-based framework that enables mobile robots to autonomously explore and generate high-fidelity 3D maps—critical for applications in rescue missions, utility tunnel monitoring, and indoor modeling. Building on this, Zou's 2023 paper "Stereo Visual Inertial Pose Estimation Based on Feedforward and Feedbacks" (13 citations) presents an innovative, computationally efficient method that uses feedforward-feedback mechanisms to achieve rapid pose estimation without the heavy storage demands of traditional filter- or optimization-based approaches. This work advances real-time navigation for resource-constrained platforms. With a growing citation record, Zou's contributions are shaping the future of autonomous exploration and mapping, offering practical solutions for high-quality spatial understanding in complex, unknown environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Stereo Visual Inertial Pose Estimation Based on Feedforward and Feedbacks13 citations · 2023