Papers
1
Total Citations
2
H-Index
1
About
Shaolei Lu is a robotics researcher whose work centers on autonomous navigation, simultaneous localization and mapping (SLAM), and path planning in three-dimensional environments. His key contribution is the development of a dual-stage active pose-graph SLAM framework, which intelligently balances the competing demands of rapid exploration and efficient loop-closing to reduce localization uncertainty. This approach leverages graph topology to guide a robot’s path, enabling it to both map unknown spaces and correct drift in real time. While his most-cited paper to date has garnered 2 citations, the work represents a foundational step in active SLAM—a field critical for autonomous drones, planetary rovers, and industrial inspection robots. Lu’s research addresses a core challenge in robotics: how to make a machine not just perceive its surroundings, but actively decide where to go next to improve its own understanding. By integrating path planning directly with SLAM, he offers a practical solution for robots operating in complex, three-dimensional environments where uncertainty must be minimized for safe and reliable autonomy.
Research Focus
Key Achievements
Top Papers
- 1Dual-Stage Path Planning for Active Pose-Graph SLAM by Graph Topology2 citations · 2022