Hogyun Kim

I'm a PhD student at Inha University, advised by Younggun Cho in SPARO Lab. My research focuses on scalable and collaborative robot autonomy, enabling multiple robots to perceive, understand, and navigate shared environments together.

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News

  • [26.02.01]   Accepted one paper to IEEE ICRA 2026 (2nd Author)
  • [26.01.23]   Awarded the Silver Prize in 32nd Samsung Humantech Paper Award (1st Author)
  • [26.01.16]   Submitted one paper to IJRR 2026 (1st Author)
  • [25.08.29]   Awarded NRF Doctoral Research Fellowship (Principal Investigator)
  • [25.08.05]   Invited for IEEE T-FR Special Issue (1st Author)
  • [25.06.16]   Accepted one paper to IEEE/RSJ IROS 2025 (2nd Author)
  • [25.06.01]   Accepted one paper to IEEE T-IV 2025 (2nd Author)
  • [25.04.30]   Selected for Spotlight Talk at ICRA 2025 Workshop on Field Robotics (1st Author)
  • [25.01.28]   Accepted two papers to IEEE ICRA 2025 (Co-Author)
  • [24.09.18]   Accepted one paper to IEEE RA-L 2024 (1st Author)
  • [24.07.22]   Accepted one paper to IEEE RA-L 2024 (1st Author)
  • [24.05.13]   Awarded Best Paper Award (3rd Place) at ICRA 2024 Workshop on Future of Construction (2nd Author)
  • [23.05.16]   Accepted one paper to IEEE ICRA 2024 Workshop on Radar in Robotics (1st Author)
  • [24.01.09]   Accepted one paper to IEEE Sensors Letters 2024 (1st Author)
  • [23.05.16]   Accepted one paper to IEEE ICRA 2023 Workshop on Future of Construction (1st Author)
  • [23.01.17]   Accepted one paper to IEEE ICRA 2023 (1st Author)
  • [22.08.10]   Accepted one paper to IEEE IROS 2022 Late-Breaking (1st Author)

Research

I'm interested in robotics, SLAM, and autonomous systems. Most of my research is about enabling multiple robots to perceive and navigate shared environments, usually with multi-robot SLAM and place recognition. Some papers are highlighted.

b3do KISS-IMU: Self-supervised Inertial Odometry with Motion-balanced Learning and Uncertainty-aware Inference
Jiwon Choi, Hogyun Kim, Geonmo Yang, Juhui Lee, Younggun Cho
ICRA, 2026
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do MARSCalib: Multi-robot, Automatic, Robust, Spherical Target-based Extrinsic Calibration in Field and Extraterrestrial Environments
Seokhwan Jeong, Hogyun Kim, Younggun Cho
IROS, 2025
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do Uni-Mapper: Unified Mapping Framework for Multi-modal LiDARs in Complex and Dynamic Environments
Gilhwan Kang, Hogyun Kim, Byunghee Choi, Seokhwan Jeong Youngsik Shin Younggun Cho
T-IV, 2025
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do SKiD-SLAM: Robust, Lightweight, and Distributed Multi-Robot LiDAR SLAM in Resource-Constrained Field Environments
Hogyun Kim, Jiwon Choi, Juwon Kim, Geonmo Yang Dongjin Cho Hyungtae Lim Younggun Cho
ICRA Workshop on Field Robotics (spotlight), 2025
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot Navigation in Multi-session Outdoor Environments
Juwon Kim, Hogyun Kim, Seokhwan Jeong, Youngsik Shin Younggun Cho
ICRA, 2025
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do PoLaRIS Dataset: A Maritime Object Detection and Tracking Dataset in Pohang Canal
Jiwon Choi, Dongjin Cho, Gihyeon Lee, Hogyun Kim, Geonmo Yang, Joowan Kim Younggun Cho
ICRA, 2025
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do ReFeree: Radar-based Lightweight and Robust Localization using Feature and Free space
Hogyun Kim, Byunghee Choi, Euncheol Choi, Younggun Cho
RA-L, 2024
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do Narrowing your FOV with SOLiD: Spatially Organized and Lightweight Global Descriptor for FOV-constrained LiDAR Place Recognition
Hogyun Kim, Jiwon Choi, Taehu Sim, Giseop Kim, Younggun Cho
RA-L, 2024
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do DiTer: Diverse Terrain and Multimodal Dataset for Field Robot Navigation in Outdoor Environments
Seokhwan Jeong*, Hogyun Kim*, Younggun Cho
IEEE Sensors Letters, 2024
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
b3do Robust Imaging Sonar-based Place Recognition and Localization in Underwater Environments
Hogyun Kim, Gilhwan Kang, Seokhwan Jeong, Seungjun Ma, Younggun Cho
ICRA, 2023
Project page / arXiv / paper / code

We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.

ETC

Presentation

 KRoC OS on Cutting-Edge Tech. and App. for Autonomous Multi-Robot/Agent Collabo, 2024
IEEE RAS Summer School on Multi-Robot Systems, 2024
ICROS OS on Robotics and AI Tech. for Space and Extreme Environments, 2024

Academic Service

 Reviewer, IJRR 2026
Reviewer, RAL 2025
Reviewer, ICRA 2026
Reviewer, IROS 2025
Reviewer, IEEE JOE 2025

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