Jiahe Peng
Papers
1
Total Citations
9
H-Index
1
About
Jiahe Peng is a researcher focused on advancing visual simultaneous localization and mapping (VSLAM) for intelligent mobile robots operating in dynamic environments. His key contributions lie at the intersection of computer vision, deep learning, and robotics, particularly in improving SLAM robustness against moving objects. His most cited work, "VSLAM Optimization Method in Dynamic Scenes Based on YOLO-Fastest" (2023, 9 citations), introduces a lightweight deep learning approach that integrates YOLO-Fastest object detection to filter dynamic elements—such as pedestrians or vehicles—from the mapping process. This innovation significantly enhances localization accuracy and map consistency in real-world, cluttered settings, addressing a critical bottleneck in autonomous navigation. By leveraging semantic information to suppress dynamic interference, Peng’s method offers a practical, computationally efficient solution for resource-constrained robotic platforms. His research bridges the gap between theoretical SLAM algorithms and deployable systems, making strides toward reliable robot autonomy in unpredictable environments. With growing citation impact, Peng’s work is increasingly recognized for its potential to improve safety and performance in applications ranging from service robots to autonomous driving.
Research Focus
Key Achievements
Top Papers
- 1VSLAM Optimization Method in Dynamic Scenes Based on YOLO-Fastest9 citations · 2023