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Kunwoo Park

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

IROS Conference 2020 Conference Paper

Ultra-Wideband Aided UAV Positioning Using Incremental Smoothing with Ranges and Multilateration

  • Jungwon Kang
  • Kunwoo Park
  • Zahra Arjmandi
  • Gunho Sohn
  • Mozhdeh Shahbazi
  • Patrick Ménard

In this paper, we present a novel smoothing approach for ultra-wideband (UWB) aided unmanned aerial vehicle (UAV) positioning. Existing works based on smoothing or filtering estimate 3D position of UAV by updating a solution for each single 1D low-dimensional UWB range measurement. However, a low-dimensional single range measurement merely acts as a weak constraint in a solution space for UAV position estimation, and thus it can often lead to incorrect estimation in unfavorable conditions. Inspired by the idea that the multilateration outcome can be utilized as a measurement providing a strong constraint, we utilize two types of UWB-based measurements: (i) each single 1D range as a high-rate measurement with a weak constraint, and (ii) multilateration outcome as a low-rate measurement with a strong constraint. We propose an incremental smoothing-based method that seamlessly integrates these two types of UWB-based measurements and inertial measurement into a unified factor graph framework. Through experiments under a variety of scenarios, we demonstrate the effectiveness of the proposed method.

AAAI Conference 2019 Conference Paper

Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder

  • Seunghyun Yoon
  • Kunwoo Park
  • Joongbo Shin
  • Hongjun Lim
  • Seungpil Won
  • Meeyoung Cha
  • Kyomin Jung

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world.