Search Results - "Yongseop Jeong"

Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation by Jeong, Yongseop, Park, Jinsun, Cho, Donghyeon, Hwang, Yoonjin, Choi, Seibum B, Kweon, In So

“…Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world…”
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Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments by Jeon, Jinhwan, Hwang, Yoonjin, Jeong, Yongseop, Park, Sangdon, Kweon, In So, Choi, Seibum B.

“…With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally,…”
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Online Misalignment Estimation of Strapdown Navigation for Land Vehicle under Dynamic Condition by Hwang, Yoonjin, Jeong, Yongseop, Kweon, In So, Choi, Seibum

“…In recent times, localization and positioning techniques have rapidly developed with the increasing demand for unmanned vehicles. Most positioning systems for…”
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EureCar turbo: A self-driving car that can handle adverse weather conditions by Unghui Lee, Jiwon Jung, Seunghak Shin, Yongseop Jeong, Kibaek Park, Shim, David Hyunchul, In-so Kweon

“…Autonomous driving technology has made significant advances in recent years. In order for self-driving cars to become practical, they are required to operate…”
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Identification of Vehicle Dynamics Model and Lever-Arm for Arbitrarily Mounted Motion Sensor by Hwang, Yoonjin, Jeong, Yongseop, Kweon, In So, Choi, Seibum B.

“…This paper presents a novel identification method for vehicle dynamics model and sensor lever-arm using the motion measurements from sensors mounted on…”
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Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments by Park, Jinsun, Jeong, Yongseop, Joo, Kyungdon, Cho, Donghyeon, Kweon, In So

“…In this letter, we propose an adaptive cost volume fusion algorithm for multi-modal depth estimation in changing environments. Our method takes measurements…”
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Visuomotor Understanding for Representation Learning of Driving Scenes by Lee, Seokju, Kim, Junsik, Oh, Tae-Hyun, Jeong, Yongseop, Yoo, Donggeun, Lin, Stephen, Kweon, In So

“…Dashboard cameras capture a tremendous amount of driving scene video each day. These videos are purposefully coupled with vehicle sensing data, such as from…”
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