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Minho Oh

7 accepted papers

2026

LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization

Kevin Christiansen Marsim, Minho Oh, Byeongho Yu, Seungjae Lee, I Made Aswin Nahrendra, Hyungtae Lim +1
ICRA 2026poster

Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and localize the robot, ensuring safe and efficient operation. While prior sensor fusion-based SLAM approaches have integra…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains

Seunghyun Lee, I Made Aswin Nahrendra, Dongkyu Lee, Byeongho Yu, Minho Oh, Hyeonwoo Lee +1
ICRA 2025

Recent advances in quadrupedal robots have demonstrated impressive agility and the ability to traverse diverse terrains. However, hardware issues, such as motor overheating or joint locking, may occur during long-distance walking or traversing through rough terrains leading to locomotion failures. A

Cited by 3Source ↗Scholar ↗
2025

LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization

Kevin Christiansen Marsim, Minho Oh, Byeongho Yu, Seungjae Lee, I Made Aswin Nahrendra, Hyungtae Lim +1
RA-L 2025

Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and localize the robot, ensuring safe and efficient operation. While prior sensor fusion-based SLAM approaches have integra

Cited by 3Source ↗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 1Source ↗Scholar ↗
2025

TRG-Planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation

Dongkyu Lee, I Made Aswin Nahrendra, Minho Oh, Byeongho Yu, Hyun Myung
RA-L 2025

Unstructured environments such as mountains, caves, construction sites, or disaster areas are challenging for autonomous navigation because of terrain irregularities. In particular, it is crucial to plan a path to avoid risky terrain and reach the goal quickly and safely. In this paper, we propose a

Cited by 18Source ↗code ↗Scholar ↗
2022

TRAVEL: Traversable Ground and Above-Ground Object Segmentation Using Graph Representation of 3D LiDAR Scans

Minho Oh, Euigon Jung, Hyungtae Lim, Wonho Song, Sumin Hu, Eungchang Mason Lee +4
RA-L 2022

Perception of traversable regions and objects of interest from a 3D point cloud is one of the critical tasks in autonomous navigation. A ground vehicle needs to look for traversable terrains that are explorable by wheels. Then, to make safe navigation decisions, the segmentation of objects positione

Cited by 55Source ↗code ↗Scholar ↗
2021

Patchwork: Concentric Zone-Based Region-Wise Ground Segmentation With Ground Likelihood Estimation Using a 3D LiDAR Sensor

Hyungtae Lim, Minho Oh, Hyun Myung
RA-L 2021

Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. Unfortunately, the ground is not flat, as it features steep slopes; bumpy roads; or objects, such as curbs, flower beds, and so forth. To tackle the problem, this letter presents

Cited by 160Source ↗code ↗Scholar ↗