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
625. Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Antyanta Bangunharcana, Jae Won Cho, Seokju Lee, In So Kweon, Kyung-Soo Kim, Soohyun Kim
International Conference on Intelligent Robots and Systems, IROS, 2021 2021 / 06
624. Deep Volumetric Depth Fusion for 3D Scene Reconstruction
Jaesung Choe, Sunghoon Im, Francois Rameau, Minjun Kang, and In So Kweon
IEEE International Conference on Computer Vision (ICCV) 2021 / 10
623. Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Seokju Lee, Francois Rameau, Fei Pan, and In So Kweon
IEEE International Conference on Computer Vision (ICCV) 2021 / 10
622. LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
Inkyu Shin, Dong-Jin Kim, Jae Won Cho, Sanghyun Woo, Kwanyong Park, and In So Kweon
IEEE International Conference on Computer Vision (ICCV) 2021 / 10
621. MS-UDA:Multi-Spectral Unsupervised Domain Adaptation for Thermal Image Semantic Segmentation
Yeong-Hyeon Kim, Ukcheol Shin, Jinsun Park, In So Kweon
IEEE Robotics and Automation Letters 2021 / 06
620. Depth Completion using Plane-Residual Representation
Byeong-Uk Lee, Kyunghyun Lee and In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2021 2021 / 06
619. Learning to Associate Every Segment for Video Panoptic Segmentation
Sanghyun Woo, Dahun Kim, Joon-Young Lee and In So Kweon
Computer Vision and Pattern Recognition, CVPR, 2021 2021 / 06
618. Volumetric Propagation Network: Stereo-LiDAR Fusion for Long Range Depth Estimation
Jaesung Choe, Kyungdon Joo, Imtiaz Tooba, In So Kweon
IEEE Robotics and Automation Letters (RA-L) 2021 / 06
617. Stereo Object Matching Network
{Jaesung Choe, Kyungdon Joo}*, Francois Rameau, and In So Kweon
IEEE International Conference on Robotics and Automation (ICRA) 2021 / 06
616. Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
{Chaoning Zhang, Philipp Benz}*, Adil Karjauv, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
615. Optical Flow Estimation from a Single Motion-blurred Image
Dawit Mureja Argaw, Junsik Kim, Francois Rameau, Jae Won Cho, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
614. Motion-blurred Video Interpolation and Extrapolation
Dawit Mureja Argaw, Junsik Kim, Francois Rameau, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
613. Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
Seokju Lee, Sunghoon Im, Stephen Lin, In So Kweon
Association for the Advancement of Artificial Intelligence (AAAI) 2021 / 02
612. ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang*, Philipp Benz*, Dawit Mureja Argaw, Seokju Lee, Junsik Kim, Francois Rameau, Jean-Charles Bazin, In So Kweon (*: equal contribution)
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 1
611. High-quality Frame Interpolation via Tridirectional Inference
Jinsoo Choi, Jaesik Park, and In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 01
610. Revisiting Batch Normalization for Improving Corruption Robustness
Philipp Benz*, Chaoning Zhang*, Adil Karjauv, and In So Kweon (*: equal contribution)
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 01
609. The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation
Myungchul Kim, Sanghyun Woo, Dahun Kim, and In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV) 2021 / 01
608. UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
Chaoning Zhang*, Philipp Benz*, Adil Karjauv*, Geng Sun, In-So Kweon (*: equal-contribution)
NeurIPS, 2020 2020 / 12
607. Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
KwanYong Park, Sanghyun Woo, Inkyu Shin, In So Kweon
NeurIPS, 2020 2020 / 12
606. An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon
NeurIPS, 2020 2020 / 12
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