Papers
7
Total Citations
48
H-Index
5
About
Jiaqi Zhao is a researcher advancing the frontiers of autonomous robotics, with a focus on LiDAR-based SLAM, 3D point cloud perception, and robotic manipulation. Their work addresses critical challenges in robot navigation and scene understanding, particularly in environments where traditional methods fail. Zhao’s most cited paper, “A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System” (2023, 13 citations), tackles degeneracy in LiDAR SLAM caused by insufficient structural features, proposing a novel virtual inertial system to ensure robust localization and mapping. This contribution is vital for reliable autonomous navigation in complex settings. In “Path Planning based on Multi-objective Topological Map” (2021, 9 citations), Zhao improves multi-objective path planning efficiency, overcoming scalability limitations for optimal robot routes. Their work on multi-modal fusion is equally impactful: “Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception” (2024, 7 citations) enhances segmentation accuracy by integrating camera and LiDAR data, while “Joint Segmentation and Grasp Pose Detection with Multi-Modal Feature Fusion Network” (2023, 7 citations) enables efficient robotic grasping in cluttered scenes. Zhao also explores adversarial robustness in “Adversarial Geometric Attacks for 3D Point Cloud Object Tracking” (2025, 4 citations), strengthening tracking models for autonomous driving. With a growing citation record and a focus on practical, real-world applications, Zhao is a rising contributor to intelligent robotics and perception systems.
Research Focus
Key Achievements
Top Papers
- 1A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System13 citations · 2023
- 2Path Planning based on Multi-objective Topological Map9 citations · 2021
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- 6Adversarial Geometric Attacks for 3D Point Cloud Object Tracking4 citations · 2025
- 7A Design of Micromouse Based on ARM3 citations · 2022