Papers
6
Total Citations
724
H-Index
6
About
Haoyang Ye is a leading researcher in robotics, specializing in state estimation, sensor fusion, and simultaneous localization and mapping (SLAM). His work centers on enabling robust and precise ego-motion estimation for autonomous mobile robots, particularly through the tight integration of 3D LiDAR and inertial measurement units (IMUs). Ye’s seminal 2019 paper, “Tightly Coupled 3D Lidar Inertial Odometry and Mapping,” has garnered over 568 citations, establishing a foundational approach for jointly minimizing errors in LiDAR-IMU systems to compensate for the weaknesses of individual sensors. He further advanced the field by addressing the challenge of multi-LiDAR configurations, proposing a system for robust online extrinsic calibration and odometry in his 2021 work, which has received 124 citations. Ye also developed LINS and R-LINS, lightweight, robocentric state estimators that leverage an iterated error-state Kalman filter for efficient and resilient navigation in demanding environments. Demonstrating versatility, his research extends to medical robotics, where he applied PCA-aided fully convolutional networks for semantic segmentation of multi-channel fMRI data. Through these contributions, Ye has significantly enhanced the reliability and perceptual awareness of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Tightly Coupled 3D Lidar Inertial Odometry and Mapping568 citations · 2019
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- 4LINS: A Lidar-Inerital State Estimator for Robust and Fast Navigation7 citations · 2019
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