Jeewon Kim — accepted AI-conference papers

2025

SaWa-ML: Structure-Aware Pose Correction and Weight Adaptation-Based Robust Multi-Robot Localization

Junho Choi, Kihwan Ryoo, Jeewon Kim, Taeyun Kim, Eungchang Mason Lee, Myeongwoo Jeong +3

IROS 2025

Multi-robot localization is a crucial task for implementing multi-robot systems. Numerous researchers have proposed optimization-based multi-robot localization methods that use camera, IMU, and UWB sensors. Nevertheless, characteristics of individual robot odometry estimates and distance measurement

Cited by 0 Source ↗Scholar ↗

2025

TACS-Graphs: Traversability-Aware Consistent Scene Graphs for Ground Robot Localization and Mapping

Jeewon Kim, Minho Oh, Hyun Myung

IROS 2025

Scene graphs have emerged as a powerful tool for robots, providing a structured representation of spatial and semantic relationships for advanced task planning. Despite their potential, conventional 3D indoor scene graphs face critical limitations, particularly under- and over-segmentation of room l

Cited by 1 Source ↗Scholar ↗