About

Soohwan Kim is a robotics researcher whose work spans probabilistic mapping, vision-based localization, and human-robot interaction. He is perhaps best known for his contributions to Gaussian process-based robotic mapping, most notably the GPmap framework (2014, 47 citations), which established a unified approach to spatial representation using sparse Gaussian processes. Building on this foundation, his work on recursive Bayesian updates for occupancy mapping and surface reconstruction introduced an elegant single-framework solution capable of generating both occupancy maps and surface meshes, advancing the field beyond batch-processing limitations. Kim's earlier research made significant strides in vision-based global localization, developing hybrid map representations that combine topological and metric approaches to help mobile robots reliably determine their position in complex indoor environments (2009, 32 citations). His contextual object recognition work, particularly for door detection, demonstrated how incorporating robot-specific contextual knowledge can meaningfully improve recognition efficiency. More recently, Kim has addressed the growing demand for multimodal sensing with a targetless, structureless approach to spatiotemporal camera-LiDAR calibration. Across his career, his research reflects a consistent drive to make robotic perception more robust, scalable, and practically deployable — contributions that continue to influence both academic robotics research and real-world autonomous systems development.

Research Focus

Key Achievements

6
H-Index
13
Papers
156
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
GPmap: A Unified Framework for Robotic Mapping Based on Sparse Gaussian Processes
47 citations · 2014
📈 Most Prolific Year: 2009 (3 Papers)
🤝 Key Collaborators: 17
🏛 Institutions: Australian National University, Korea Institute of Science and Technology, Sun Moon University, Korea Institute of Robot and Convergence

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago