Papers
6
Total Citations
32
H-Index
4
About
Yiming Nie is a leading researcher in autonomous driving and mobile robotics, with a primary focus on LiDAR-based perception, localization, and world modeling. His most impactful work, "UniWorld: Autonomous Driving Pre-training via World Models" (2023, 8 citations), draws inspiration from classic occupancy grid theory to develop a spatial-temporal world model that enables robots to perceive their environment and predict future behaviors—a foundational contribution to self-supervised driving pre-training. Nie has also advanced LiDAR odometry and mapping through a two-stage feature extraction method for real-time 6-DoF pose estimation, and developed a fast calibration approach for onboard LiDAR-camera systems (6 citations), critical for outdoor surveillance and security robots. More recently, his work on rotation-robust place recognition (R2SCAT-LPR, 2025, 5 citations) and semantic-guided LiDAR-based place recognition (SG-LPR, 2024, 4 citations) has pushed the boundaries of loop closure detection and re-localization in large-scale environments. His novel three-layer-architecture planning method for multi-heterogeneous autonomous land vehicles further demonstrates his breadth in multi-robot systems. With over 30 citations across his key publications, Nie’s research is shaping the future of robust, real-time autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1UniWorld: Autonomous Driving Pre-training via World Models8 citations · 2023
- 2Lidar Odometry and Mapping Based on Two-stage Feature Extraction7 citations · 2020
- 3A fast calibration approach for onboard LiDAR-camera systems6 citations · 2020
- 4
- 5SG-LPR: Semantic-Guided LiDAR-Based Place Recognition4 citations · 2024
- 6