Papers
9
Total Citations
76
H-Index
5
About
Yisha Liu is a leading researcher in robotics and autonomous systems, with a primary focus on simultaneous localization and mapping (SLAM), multi-sensor fusion, and robot calibration. Her work addresses fundamental challenges in enabling robots to perceive, navigate, and operate reliably in complex environments. Liu’s most cited paper, “An overview of simultaneous localisation and mapping: towards multi-sensor fusion” (2023, 27 citations), provides a comprehensive survey of SLAM evolution, highlighting the critical shift toward integrating diverse sensors for robust performance. She has made significant contributions to indoor mobile robot navigation, developing methods for instant map building and localization using 2D laser scanning data (2012, 17 citations). Her research also tackles practical industrial challenges, such as compensating non-geometric errors in long-stroke Cartesian robots through a hybrid analytical and data-driven approach (2021, 8 citations). Liu has advanced tightly coupled laser-inertial pose estimation using B-spline curves (2023, 7 citations) and introduced a “look-backward-and-forward” adaptation strategy for assessing parameter estimation errors in human motion prediction models (2022, 5 citations), directly impacting safe human-robot collaboration. Her work on outdoor scene understanding using multi-scale panoramic bearing angle images and point cloud features (2019, 3 citations) further extends her impact to autonomous vehicles. With a growing citation record and contributions spanning from foundational SLAM theory to practical calibration and motion planning, Yisha Liu is shaping the future of intelligent, perceptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9