Search Results for author: Hyungtae Lim

Found 9 papers, 5 papers with code

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

1 code implementation25 Jul 2022 Seungjae Lee, Hyungtae Lim, Hyun Myung

Moreover, even if the parameters are well adjusted, a partial under-segmentation problem can still emerge, which implies ground segmentation failures in some regions.

Object Recognition

eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-

no code implementations19 Jul 2022 Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, Hyun Myung

Thanks to our clustering approach in spatio-temporal space, our method automatically solves feature detection and feature tracking simultaneously.

PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

1 code implementation1 Jun 2022 Dong-Uk Seo, Hyungtae Lim, Seungjae Lee, Hyun Myung

In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method.

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

no code implementations13 Mar 2022 Hyungtae Lim, Suyong Yeon, Soohyun Ryu, Yonghan Lee, Youngji Kim, JaeSeong Yun, Euigon Jung, Donghwan Lee, Hyun Myung

As verified in indoor and outdoor 3D LiDAR datasets, our proposed method yields robust global registration performance compared with other global registration methods, even for distant point cloud pairs.

Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor

2 code implementations12 Aug 2021 Hyungtae Lim, Minho Oh, Hyun Myung

Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition.

Object Recognition

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

no code implementations11 Aug 2021 Hyungyu Lee, Myeongwoo Jeong, Chanyoung Kim, Hyungtae Lim, Changgue Park, Sungwon Hwang, Hyun Myung

In this paper, a novel reinforcement learning-based method is proposed to control a tilting multirotor on real-world applications, which is the first attempt to apply reinforcement learning to a tilting multirotor.

reinforcement-learning

Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network

2 code implementations18 Jun 2021 Sungwon Hwang, Hyungtae Lim, Hyun Myung

Training a Convolutional Neural Network (CNN) to be robust against rotation has mostly been done with data augmentation.

Data Augmentation Image Classification +2

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

3 code implementations7 Mar 2021 Hyungtae Lim, Sungwon Hwang, Hyun Myung

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.

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

no code implementations4 Aug 2020 Hyungtae Lim, Hyeonjae Gil, Hyun Myung

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.

Depth Estimation Depth Prediction

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