no code implementations • 18 May 2024 • Hyungtae Lim
In particular, because learning-based methods are prone to catastrophic failure when operated in untrained scenes, there is still a demand for conventional yet robust approaches that work out of the box in diverse scenarios, such as real-world robotic services and SLAM competitions.
no code implementations • 16 Apr 2024 • Changki Sung, Wanhee Kim, Jungho An, Wooju Lee, Hyungtae Lim, Hyun Myung
Despite great improvements in semantic segmentation, challenges persist because of the lack of local/global contexts and the relationship between them.
1 code implementation • 19 Dec 2023 • Wooju Lee, Dasol Hong, Hyungtae Lim, Hyun Myung
To address these problems, we propose an object-aware domain generalization (OA-DG) method for single-domain generalization in object detection.
Ranked #1 on Robust Object Detection on DWD
no code implementations • 17 Apr 2023 • Alex Junho Lee, Seungwon Song, Hyungtae Lim, Woojoo Lee, Hyun Myung
To this end, LiDAR measurements are expressed in the form of range images before matching them to reduce the modality discrepancy.
2 code implementations • 25 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.
no code implementations • 19 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.
1 code implementation • 1 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.
no code implementations • 13 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.
2 code implementations • 12 Aug 2021 • Hyungtae Lim, Minho Oh, Hyun Myung
Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition.
no code implementations • 11 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.
2 code implementations • 18 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.
3 code implementations • 7 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.
no code implementations • 4 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.