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Hyungtae Lim

31 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

BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes

Minkyun Seo, Hyungtae Lim, Kanghee Lee, Luca Carlone, Jaesik Park
ICCV 2025poster

Recent advances in deep learning-based point cloud registration have improved generalization, yet most methods still require retraining or manual parameter tuning for each new environment. In this paper, we identify three key factors limiting generalization: (a) reliance on environment-specific voxe…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

GenZ-ICP: Generalizable and Degeneracy-Robust LiDAR Odometry Using an Adaptive Weighting

Daehan Lee, Hyungtae Lim, Soohee Han
RA-L 2025

Light detection and ranging (LiDAR)-based odometry has been widely utilized for pose estimation due to its use of high-accuracy range measurements and immunity to ambient light conditions. However, the performance of LiDAR odometry varies depending on the environment and deteriorates in degenerative

Cited by 39Source ↗code ↗Scholar ↗
2025

KISS-Matcher: Fast and Robust Point Cloud Registration Revisited

Hyungtae Lim, Daebeom Kim, Gunhee Shin, Jingnan Shi, Ignacio Vizzo, Hyun Myung +2
ICRA 2025

While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-sou

Cited by 19Source ↗code ↗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

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 0Source ↗Scholar ↗
2024

CLOi-Mapper: Consistent, Lightweight, Robust, and Incremental Mapper With Embedded Systems for Commercial Robot Services

DongKi Noh, Hyungtae Lim, Gyuho Eoh, Duckyu Choi, Jeongsik Choi, Hyunjun Lim +2
RA-L 2024

In commercial autonomous service robots with several form factors, simultaneous localization and mapping (SLAM) is an essential technology for providing proper services such as cleaning and guidance. Such robots require SLAM algorithms suitable for specific applications and environments. Hence, seve

Cited by 3Source ↗Scholar ↗
2024

Contextrast: Contextual Contrastive Learning for Semantic Segmentation

Changki Sung, Wanhee Kim, Jungho An, Wooju Lee, Hyungtae Lim, Hyun Myung
CVPR 2024poster

Despite great improvements in semantic segmentation challenges persist because of the lack of local/global contexts and the relationship between them. In this paper we propose Contextrast a contrastive learning-based semantic segmentation method that allows to capture local/global contexts and compr…

Cited by 18Source ↗PDF ↗Scholar ↗
2024

DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery

Seungwon Song, Hyungtae Lim, Alex Junho Lee, Hyun Myung
RA-L 2024

Despite extensive research in robust visual-inertial navigation systems (VINS) in dynamic environments, many approaches remain vulnerable to objects that suddenly start moving, which are referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">

Cited by 11Source ↗Scholar ↗
2024

HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors

Hyungtae Lim, Seoyeon Jang, Benedikt Mersch, Jens Behley, Hyun Myung, Cyrill Stachniss
IROS 2024

Moving object segmentation (MOS) using a 3D light detection and ranging (LiDAR) sensor is crucial for scene understanding and identification of moving objects. Despite the availability of various types of 3D LiDAR sensors in the market, MOS research still predominantly focuses on 3D point clouds fro

Cited by 13Source ↗Scholar ↗
2024

Object-Aware Domain Generalization for Object Detection

Wooju Lee, Dasol Hong, Hyungtae Lim, Hyun Myung
AAAI 2024technical

Single-domain generalization (S-DG) aims to generalize a model to unseen environments with a single-source domain. However, most S-DG approaches have been conducted in the field of classification. When these approaches are applied to object detection, the semantic features of some objects can be dam…

Cited by 18Source ↗PDF ↗code ↗Scholar ↗
2023

(LC)${2}$: LiDAR-Camera Loop Constraints for Cross-Modal Place Recognition

Alex Junho Lee, Seungwon Song, Hyungtae Lim, Woojoo Lee, Hyun Myung
RA-L 2023

Localization has been a challenging task for autonomous navigation. A loop detection algorithm must overcome environmental changes for the place recognition and re-localization of robots. Therefore, deep learning has been extensively studied for the consistent transformation of measurements into loc

Cited by 28Source ↗Scholar ↗
2023

ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes

Hyungtae Lim, Lucas Nunes, Benedikt Mersch, Xieyuanli Chen, Jens Behley, Hyun Myung +1
RSS 2023poster

A map of the environment is an essential component for robotic navigation. In the majority of cases, a map of the static part of the world is the basis for localization, planning, and navigation. However, dynamic objects that are presented in the scenes during mapping leave undesirable traces in the…

Cited by 28Source ↗PDF ↗code ↗Scholar ↗
2023

Enhancing Robustness of Line Tracking Through Semi-Dense Epipolar Search in Line-Based SLAM

Dong-Uk Seo, Hyungtae Lim, Eungchang Mason Lee, Hyunjun Lim, Hyun Myung
IROS 2023poster

Line information from urban structures can be exploited as an additional geometrical feature to achieve robust vision-based simultaneous localization and mapping (SLAM) systems in textureless scenes. Sometimes, however, conventional line tracking methods fail to track caused by image blur or occlusi…

Cited by 1Source ↗Scholar ↗
2023

ORORA: Outlier-Robust Radar Odometry

Hyungtae Lim, Kawon Han, Gunhee Shin, Giseop Kim, Songcheol Hong, Hyun Myung
ICRA 2023poster

Radar sensors are emerging as solutions for perceiving surroundings and estimating ego-motion in extreme weather conditions. Unfortunately, radar measurements are noisy and suffer from mutual interference, which degrades the performance of feature extraction and matching, triggering imprecise matchi…

Cited by 25Source ↗code ↗Scholar ↗
2023

X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments

DongKi Noh, Changki Sung, Taeyoung Uhm, Wooju Lee, Hyungtae Lim, Jaeseok Choi +9
RA-L 2023

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other comput

Cited by 7Source ↗Scholar ↗
2022

A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments

Hyungtae Lim, Suyong Yeon, Soohyun Ryu, Yonghan Lee, Youngji Kim, Jaeseong Yun +3
ICRA 2022poster

Global registration using 3D point clouds is a crucial technology for mobile platforms to achieve localization or manage loop-closing situations. In recent years, numerous researchers have proposed global registration methods to address a large number of outlier correspondences. Unfortunately, the d…

Cited by 47Source ↗code ↗Scholar ↗
2022

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

Hyungtae Lim, Hanseok Ryu, Matthew B. Rhudy, Dongjin Lee, Dongjin Jang, Changho Lee +3
RA-L 2022

A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic U

Cited by 10Source ↗Scholar ↗
2022

Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud

Seungjae Lee, Hyungtae Lim, Hyun Myung
IROS 2022poster

In the field of 3D perception using 3D LiDAR sensors, ground segmentation is an essential task for various purposes, such as traversable area detection and object recognition. Under these circumstances, several ground segmentation methods have been proposed. However, some limitations are still encou…

Cited by 155Source ↗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 ↗
2022

eCDT: Event Clustering for Simultaneous Feature Detection and Tracking

Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, Hyun Myung
IROS 2022poster

Contrary to other standard cameras, event cam-eras interpret the world in an entirely different manner; as a collection of asynchronous events. Despite event camera's unique data output, many event feature detection and tracking algorithms have shown significant progress by making detours to frame-b…

Cited by 26Source ↗Scholar ↗
2021

ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building

Hyungtae Lim, Sungwon Hwang, Hyun Myung
RA-L 2021

Scan data of urban environments often include representations of dynamic objects, such as vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud map with sequential accumulations of the scan data, the dynamic objects often leave unwanted traces in the map. These

Cited by 218Source ↗Scholar ↗
2021

Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning

Hyungyu Lee, Myeongwoo Jeong, Chanyoung Kim, Hyungtae Lim, Changgue Park, Sungwon Hwang +1
IROS 2021poster

Recently, needs for unmanned aerial vehicles (UAVs) that are attachable to the wall have been highlighted. As one of the ways to address the need, researches on various tilting multirotors that can increase maneuverability has been employed. Unfortunately, existing studies on the tilting multirotors…

Cited by 9Source ↗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 ↗
2021

REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs

Eungchang Mason Lee, Junho Choi, Hyungtae Lim, Hyun Myung
IROS 2021poster

Exploring an unknown environment without colliding with obstacles is one of the essentials of autonomous vehicles to perform diverse missions such as structural inspections, rescues, deliveries, and so forth. Therefore, unmanned aerial vehicles (UAVS), which are fast, agile, and have high degrees of…

Cited by 41Source ↗code ↗Scholar ↗
2021

State Estimation for HALE UAVs With Deep-Learning-Aided Virtual AOA/SSA Sensors for Analytical Redundancy

Wonkeun Youn, Hyungtae Lim, Hyoungsik Choi, Matthew B. Rhudy, Hyeok Ryu, Sungyug Kim +1
RA-L 2021

High-altitudelong-endurance (HALE) unmanned aerial vehicles (UAVs) are employed in a variety of fields because of their ability to fly for a long time at high altitudes, even in the stratosphere. Two paramount concerns exist: enhancing their safety during long-term flight and reducing their weight a

Cited by 17Source ↗Scholar ↗
2020

MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks

Hyungtae Lim, Hyeonjae Gil, Hyun Myung
IROS 2020poster

In this study, a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. Our proposed network consists of a multi-stage encoder-decoder architecture and Cross Stage Feat…

Cited by 6Source ↗Scholar ↗
2019

RONet: Real-time Range-only Indoor Localization via Stacked Bidirectional LSTM with Residual Attention

Hyungtae Lim, Changgue Park, Hyun Myung
IROS 2019poster

In this study, a three-layered bidirectional Long Short-term Memory (Bi-LSTM) with residual attention, named as RONet, is proposed to achieve localization using range measurements. Accordingly, we acquired our own datasets and tested RONet using realistic conditions. It is shown that the RONet can e…

Cited by 19Source ↗Scholar ↗