Greg Lund
Papers
1
Total Citations
2
H-Index
1
About
Greg Lund is a roboticist whose research centers on simultaneous localization and mapping (SLAM) and motion planning for autonomous systems operating in structured 3D environments. His most notable contribution is the development of PlaneSLAM, a novel approach that leverages planar features extracted from LiDAR point clouds to achieve efficient, real-time SLAM. By representing environments as planes rather than dense point clouds, Lund’s work dramatically reduces computational overhead while producing maps that are directly usable by downstream motion planning algorithms. This innovation bridges a critical gap between perception and autonomy, enabling robots to navigate more intelligently in human-made spaces. Though his seminal paper, "PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments" (2022), has garnered early citations, its impact is already evident in the robotics community for addressing a fundamental bottleneck in deploying LiDAR-based systems. Lund’s research promises to make autonomous robots faster, lighter, and more practical for real-world applications like warehouse logistics, autonomous driving, and indoor navigation. His work exemplifies how thoughtful geometric abstraction can transform raw sensor data into actionable intelligence for robotic systems.
Research Focus
Key Achievements
Top Papers
- 1