Robotics and Computer Vision Lab

Publications

Extra Form
저 자 Dongshin Kim, Seunghak Shin, In So Kweon
학 회 IEEE Transactions on Automation Science and Engineering (TASE)
Notes accepted
논문일시(Year) 2017
논문일시(Month) 11
Inertial Measurement Units (IMU) are successfully utilized to compensate localization errors in sensor fused inertial navigation systems. An IMU generally produces high frequency signals ranging from hundreds to thousands of Hz, and preintegration methods are applied to effectively process these high frequency signals for inertial navigation systems. The main problem with an existing preintegration method is that the inertial propagation models in the method are only generated at the IMU's coordinate system. Hence, the models have to be converted to the coordinate system of the other sensor in order to apply its constraint. So the iterative optimization framework using the conventional method takes large amount of time. In addition, since a general rigid body transformation can not transfer a velocity propagation model to the other coordinate system, the concept of relative motion analysis needs to be considered. To solve the problems above, in this paper, we propose a novel relative preintegration method that can generate inertial propagation models at any sensor's coordinate system in a rigid body. This permits accurate and fast IMU processing in sensor fused inertial navigation systems. We applied new non-linear optimization frameworks to solve initialization and extrinsic calibration problems for the IMU-IMU, IMU-Camera, and IMU-LiDAR pair based on the proposed relative preintegration method in an on-line manner, and the superior results of the mentioned processes are presented as well.

List of Articles
119. Recurrent Temporal Aggregation Framework for Deep Video Inpainting
Dahun Kim*, Sanghyun Woo*, Joon-Young Lee, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 / 11
118. Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 / 10
117. Ring Difference Filter for Fast and Noise Robust Depth from Focus
Hae-Gon Jeon, Jaeheung Surh, Sunghoon Im, In So Kweon
IEEE Transactions Image Processing (TIP) 2019 / 8
116. Deep Iterative Frame Interpolation for Full-frame Video Stabilization
Jinsoo Choi, In So Kweon
ACM Transactions on Graphics (TOG) / SIGGRAPH Asia 2019 / 11
115. One-Day Outdoor Photometric Stereo Using Skylight Estimation
Jiyoung Jung, Joon-Young Lee, In So Kweon
International Journal of Computer Vision (IJCV) 2019 / 8
114. Globally Optimal Inlier Set Maximization for Atlanta World Understanding
Kyungdon Joo, Tae-Hyun Oh, In So Kweon, Jean-Charles Bazin
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 / 3
113. High-Fidelity Depth Upsampling Using the Self-Learning Framework
Inwook Shim, Tae-Hyun Oh, In So Kweon
Sensors 2019 / 01
112. Robust Depth Estimation using Auto-Exposure Bracketing
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Transaction on Image Processing (TIP) 2018 / 12
111. Semi-calibrated Photometric Stereo
Donghyeon Cho, Yasuyuki Matsushita, Yu-Wing Tai, and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) /
110. Deep Convolutional Neural Network for Natural Image Matting using Initial Alpha Mattes
Donghyeon Cho, Yu-Wing Tai, In So Kweon
IEEE Transactions on Image Processing (TIP), accepted /
109. Real-Time Head Pose Estimation using Multi-Task Deep Neural Network
Byungtae Ahn, Dong-Geol Choi, Jaesik Park, In So Kweon
Robotics and Autonomous Systems 2018 / 05
108. Depth from a Light Field Image with Learning-based Matching Costs
Hae-Gon Jeon, Jaesik Park, Gyeongmin Choe, Jinsun Park, Yunsu Bok, Yu-Wing Tai and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 / 01.15
107. Accurate 3D Reconstruction from Small Motion Clip for Rolling Shutter Cameras
Sunghoon Im, Hyowon Ha, Gyeongmin Choe, Hae-Gon Jeon, Kyungdon Joo, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 / 03
106. KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving
Yukyung Choi, Namil Kim, Soonmin Hwang, Kibaek Park, Jae Shin Yoon, Kyunghwan An and In So Kweon
Transactions on Intelligent Transportation Systems (T-ITS) 2018 / 03
105. RANUS: RGB and NIR Urban Scene Dataset for Deep Scene Parsing
Gyeongmin Choe, Seong-heum Kim, Sunghoon Im, Joon-Young Lee, Srinivasa Narasimhan, In So Kweon
IEEE Robotics and Automation Letters (RAL) 2018 / 02
104. Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Oleksandr Bailo , Francois Rameau , Kyungdon Joo , Jinsun Park , Oleksandr Bogdan , In So Kweon
Pattern Recognition Letters (PRL) 2018 / 02
» On-line Initialization and Extrinsic Calibration of an Inertial Navigation System with a Relative Preintegration Method on Manifold
Dongshin Kim, Seunghak Shin, In So Kweon
IEEE Transactions on Automation Science and Engineering (TASE) 2017 / 11
102. Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Kyungdon Joo, Tae-Hyun Oh, Junsik Kim, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2017 / 11
101. Geometry Guided 3D Propagation for Depth from Small Motion
Seunghak Shin, Sunghoon Im, Inwook Shim, Hae-Gon Jeon, In So Kweon
IEEE Signal Processing Letters 2017 / 12
100. A Closed-Form Solution to Rotation Estimation for Structure from Small Motion
Hyowon Ha, Tae-Hyun Oh, In So Kweon
IEEE Signal Processing Letters 2017 / 05
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