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
563. Gated Bidirectional Feature Pyramid Network for Accurate One Shot Detection
Sanghyun Woo, Soonmin Hwang, Ho-Deok Jang, In So Kweon
Machine Vision And Applications (MVA) 2019 / 1
562. High-Fidelity Depth Upsampling Using the Self-Learning Framework
Inwook Shim, Tae-Hyun Oh, In So Kweon
Sensors 2019 / 01
561. DPSNet: End-to-end Deep Plane Sweep Stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon
International Conference on Learning Representations (ICLR) 2019 / 05
560. Robust Depth Estimation using Auto-Exposure Bracketing
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Transaction on Image Processing (TIP) 2018 / 12
559. Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling
Arda Senocak, Tae-Hyun Oh, Junsik Kim, In So Kweon
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2018 / 06
558. Discriminative Feature Learning for Unsupervised Video Summarization
Yunjae Jung, Donghyeon Cho, Dahun Kim, Sanghyun Woo, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2019 / 01
557. Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Dahun Kim, Donghyeon Cho, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2019 / 01
556. Semi-calibrated Photometric Stereo
Donghyeon Cho, Yasuyuki Matsushita, Yu-Wing Tai, and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) /
555. 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 /
554. LinkNet: Relational Embedding for Scene Graph
Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
Neural Information Processing Systems (NIPS) 2018 / 12
553. CBAM: Convolutional Block Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon
European Conference on Computer Vision (ECCV) 2018 / 09
552. BAM: Bottleneck Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon
British Machine Vision Conference (BMVC) 2018 / 09
551. Robust Low-rank Optimization with Priors
Tae-Hyun Oh
KAIST 2017 / 5
550. EPINET: A Fully-Convolutional Neural Network using Epipolar Geometry for Depth from Light Field Images
Changha Shin, Hae-Gon Jeon, Youngjin Yoon, In So Kweon, Seon Joo Kim
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
549. Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
548. Robust Depth Estimation from Auto Bracketed Images
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
547. Learning to Localize Sound Source in Visual Scenes
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 2018 / 06
546. Globally Optimal Inlier Set Maximization for Atlanta Frame Estimation
Kyungdon Joo, Tae-Hyun Oh, In So Kweon, Jean-Charles Bazin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 / 06
545. 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
544. 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
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