Hao Teng
Papers
2
Total Citations
7
H-Index
2
About
Hao Teng is a robotics researcher specializing in multi-sensor fusion, simultaneous localization and mapping (SLAM), and state estimation for autonomous systems. His work addresses critical challenges in perception and navigation for mobile robots operating in complex environments. Teng’s most cited paper, “Advancing Simultaneous Localization and Mapping with Multi-Sensor Fusion and Point Cloud De-Distortion” (2023, 4 citations), introduces a novel approach to overcoming the limitations of single-sensor obstacle detection by integrating data from multiple sensors. It also tackles motion distortion in LiDAR point clouds during synchronization and mapping, a key issue for accurate environmental reconstruction. In his related work, “A Sensor Fusion Algorithm: Improving State Estimation Accuracy for a Quadruped Robot Dog” (2022, 3 citations), Teng proposes a fusion scheme combining leg odometry pose estimation with the ORB-SLAM3 algorithm to enhance state estimation for quadruped robots, addressing drift and inaccuracies inherent in internal sensor-based methods. Though early in his career, Teng’s contributions are foundational for robust robot autonomy, offering practical solutions for real-world deployment in dynamic, unstructured settings. His research is particularly relevant for students and engineers working on SLAM, sensor integration, and legged robotics.
Research Focus
Key Achievements
Top Papers
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- 2