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Hanjun Kim

7 accepted papers

2026

SHeRLoc: Synchronized Heterogeneous Radar Place Recognition for Cross-Modal Localization

ICRA 2026poster

Despite the growing adoption of radar in robotics, the majority of research has been confined to homogeneous sensors, overlooking the integration and cross-modality challenges inherent in heterogeneous radar. This leads to significant difficulties in generalizing across diverse radar types, with mod…

2025

HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-Session Radar SLAM

ICRA 2025

Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, le

Cited by 16SourceScholar
2025

Mode-Unified Intent Estimation of a Robotic Prosthesis Using Deep-Learning

RA-L 2025

Traditional robotic knee-ankle prostheses categorize ambulation modes such as level walking, ramps, and stairs. However, human movement scales continuously across various states rather than discretely, making traditional mode classifiers inadequate for accurate intent recognition. This paper propose

Cited by 4SourceScholar
2025

Transfer Learning for Walking Speed Estimation Across Novel Prosthetic Devices and Populations

IROS 2025

Accurate walking speed estimation in lower-limb prostheses is crucial for delivering biomechanically appropriate assistance across varying speeds. However, training robust models requires extensive domain-specific, user-dependent (DEP) data, which is impractical for every new prosthesis user. This s

Cited by 0SourceScholar
2020

Probabilistic TSDF Fusion Using Bayesian Deep Learning for Dense 3D Reconstruction with a Single RGB Camera

ICRA 2020poster

In this paper, we address a 3D reconstruction problem using depth prediction from a single RGB image. With the recent advances in deep learning, depth prediction shows high performance. However, due to the discrepancy between training environment and test environment, 3D reconstruction can be vulner…

Cited by 4SourceScholar
2019

RGB-to-TSDF: Direct TSDF Prediction from a Single RGB Image for Dense 3D Reconstruction

IROS 2019poster

In this paper, we present a novel method to predict 3D TSDF voxels from a single image for dense 3D reconstruction. 3D reconstruction with RGB images has two inherent problems: scale ambiguity and sparse reconstruction. With the advent of deep learning, depth prediction from a single RGB image has a…

Cited by 13SourceScholar