Bor-Woei Kuo

About

Bor-Woei Kuo is a leading researcher in robotics and autonomous navigation, with a primary focus on Simultaneous Localization and Mapping (SLAM) for indoor environments. His most cited work, "A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map" (2011, 36 citations), addresses a critical challenge in robotics: the high computational complexity and memory demands of traditional SLAM algorithms. Kuo’s major contribution lies in developing a lightweight Rao-Blackwellized particle filter approach that uses line segment maps instead of dense grid maps, drastically reducing processing time and memory usage while maintaining accuracy. This innovation enables real-time navigation for resource-constrained robots, making SLAM more practical for widespread deployment. His work has been influential in advancing efficient robotic mapping, with applications in service robots and autonomous systems. Kuo’s research continues to shape the field of indoor robot navigation, demonstrating how algorithmic cleverness can overcome hardware limitations—a key insight for students and engineers working on embedded or real-time robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map

   36 citations · 2011

Key Collaborators

Contact & Links