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Yun-Tae Kim

4 accepted papers

2022

DH-LC: Hierarchical Matching and Hybrid Bundle Adjustment Towards Accurate and Robust Loop Closure

IROS 2022poster

A loop closure module plays an important role in visual SLAM systems, which can reduce the accumulat-ed drift. This task faces the challenges of large viewpoint changes and expensive computational costs when optimizing the global map. This paper proposes DH-LC, a novel accurate and robust loop closu…

Cited by 0SourceScholar
2021

Accurate Visual-Inertial SLAM by Feature Re-identification

IROS 2021poster

Most of the state-of-the-art visual inertial SLAM methods pay less attention to 2D-2D and 3D-2D matching with more reliable features in a long time span, which easily results in continuous estimation drift. In this paper, we propose an efficient drift-free visual-inertial SLAM method by a pose guide…

Cited by 5SourceScholar
2021

Accurate Visual-Inertial SLAM by Manhattan Frame Re-identification

IROS 2021poster

Most of the state-of-the-art visual-inertial SLAM methods pay less attention to the scene structure of man-made environments. In this paper, based on the assumption of multiple local Manhattan worlds (MWs), we propose a Manhattan frame (MF) re-identification method to build relative rotation constra…

Cited by 8SourceScholar
2021

UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching

ICCV 2021poster

Recent studies have shown that cascade cost volume can play a vital role in deep stereo matching to achieve high resolution depth map with efficient hardware usage. However, how to construct good cascade volume as well as effective sampling for them are still under in-depth study. Previous cascade-b…

Cited by 31PDFScholar