261 citations · 261 across the 6 of their papers we have counts for
6 papers
A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments
Hyungtae Lim, Suyong Yeon, Soohyun Ryu +6
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 research…
Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning
Hyungyu Lee, Myeongwoo Jeong, Chanyoung Kim +4
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 mul…
REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs
Eungchang Mason Lee, Junho Choi, Hyungtae Lim +1
Exploring an unknown environment without colliding with obstacles is one of the essentials of autonomous vehicles to perform diverse missions such as structural inspections, rescue…
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
Sungwon Hwang, Hyungtae Lim, Hyun Myung
Training a Convolutional Neural Network (CNN) to be robust against rotation has mostly been done with data augmentation. In this paper, another progressive vision of research direc…
ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building
Hyungtae Lim, Sungwon Hwang, Hyun Myung
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 clo…
MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks
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…