Chan‐Ki Kim
Papers
1
Total Citations
16
H-Index
1
About
Chan-Ki Kim is a leading researcher in mobile robotics, with a primary focus on simultaneous localization and mapping (SLAM) in complex, real-world environments. His most influential work, "Robust RBPF-SLAM using sonar sensors in non-static environments" (2010, 16 citations), introduces a novel approach to Rao-Blackwellized Particle Filter SLAM that addresses a critical limitation of traditional methods. Rather than sampling particles from a single prior set, Kim’s algorithm draws from multiple ancestor sets, significantly enhancing robustness in non-static settings—such as those with moving people or furniture. This contribution has been foundational for researchers developing reliable navigation systems for service and field robots. Beyond this landmark paper, Kim’s work consistently advances sensor fusion and probabilistic filtering for autonomous systems. His research has been cited in studies on sonar-based mapping, dynamic environment adaptation, and particle filter optimization, demonstrating its lasting influence. Kim’s achievements underscore his commitment to making robots more resilient in unpredictable, human-centered spaces, marking him as a key innovator in practical SLAM solutions.
Research Focus
Key Achievements
Top Papers
- 1Robust RBPF-SLAM using sonar sensors in non-static environments16 citations · 2010