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Junha Kim

6 accepted papers

2024

Image-Based Time-Varying Contact Force Control of Aerial Manipulator Using Robust Impedance Filter

RA-L 2024

The use of aerial manipulators for safe and efficient physical interaction with their surrounding environments has been gaining attention within the aerial robotics research community. In this letter, we present an image-based time-varying force tracking controller for an aerial manipulator conducti

Cited by 5SourceScholar
2024

Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data

ECCV 2024poster

"This paper aims to adapt the source model to the target environment, leveraging small user feedback (, labeled target data) readily available in real-world applications. We find that existing semi-supervised domain adaptation (SemiSDA) methods often suffer from poorly improved adaptation performanc…

2023

Real-Time Hetero-Stereo Matching for Event and Frame Camera With Aligned Events Using Maximum Shift Distance

RA-L 2023

Event cameras can show better performance than frame cameras in challenging scenarios, such as fast-moving environments or high-dynamic-range scenes. However, it is still difficult for event cameras to replace frame cameras in non-challenging normal scenarios. In order to leverage the advantages of

Cited by 16SourceScholar
2021

Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board

ICRA 2021poster

This paper proposes an automatic and accuracy- enhanced extrinsic calibration method for 3D LiDARs with a range offset correction, which needs only an arbitrarily-shaped single planar board. One of the most exhaustive parts of existing LiDAR calibration procedures is to manually find target objects…

Cited by 14SourcecodeScholar
2020

Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein Polynomial

ICRA 2020poster

This paper presents a new efficient algorithm which guarantees a solution for a class of multi-agent trajectory planning problems in obstacle-dense environments. Our algorithm combines the advantages of both grid-based and optimization-based approaches, and generates safe, dynamically feasible traje…

Cited by 98SourcecodeScholar