Papers
35
Total Citations
670
H-Index
13
About
In So Kweon is a pioneering researcher in autonomous robotics and computer vision, whose work spans terrain modeling, sensor fusion, mobile robot navigation, and intelligent visual perception. His early landmark contributions in the early 1990s — particularly the development of the **locus method** for building high-resolution 3D terrain representations from multiple sensors — laid foundational groundwork for autonomous robot navigation in unstructured environments, earning his 1992 paper over 168 citations. Throughout his career, Kweon has consistently advanced the capabilities of mobile robots through behavior-based architectures and active sensor fusion strategies, enabling robust operation in dynamic, real-world settings. A particularly notable thread in his research is camera exposure control for outdoor robotics: his gradient-based methods, developed across a series of influential papers from 2014 to 2019, address the critical challenge of adapting vision systems to wide dynamic range environments. More recently, Kweon has embraced deep learning, contributing CNN-based approaches to simultaneous dehazing and depth estimation. With dozens of highly cited publications bridging classical robotics and modern AI, his work remains essential reading for researchers building perception systems for autonomous vehicles and intelligent mobile platforms.
Research Focus
Key Achievements
Top Papers
- 1High-resolution terrain map from multiple sensor data168 citations · 1992
- 2
- 3
- 4Gradient-Based Camera Exposure Control for Outdoor Mobile Platforms46 citations · 2018
- 5Modeling rugged terrain by mobile robots with multiple sensors44 citations · 1991
- 6Architecture of behavior-based mobile robot in dynamic environment36 citations · 2003
- 7
- 8Behavior-based mobile robot using active sensor fusion24 citations · 2003
- 9CNN-Based Simultaneous Dehazing and Depth Estimation21 citations · 2020
- 10Extracting topographic features for outdoor mobile robots21 citations · 2002