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
665. Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
Sungsu Hur, Inkyu Shin, Kwanyong Park, Sanghyun Woo, In So Kweon
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 / 01
664. Self-supervised Monocular Depth Estimation from Thermal Images via Adversarial Multi-spectral Adaptation
Ukcheol Shin, Kwanyong Park, Byeong-Uk Lee, Kyunghyun Lee, In So Kweon
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 / 01
663. Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition
Youngjoon Jang, Youngtaek Oh, Jae Won Cho, Dong-Jin Kim, Joon Son Chung, In So Kweon
British Machine Vision Conference (BMVC) 2022 / 11
662. Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation
Yongseop Jeong, Jinsun Park, Donghyeon Cho, Yoonjin Hwang, Seibum Choi and In So Kweon
Sensors 2022 / 09
661. Self-supervised Monocular Depth and Motion Learning in Dynamic Scenes: Semantic Prior to Rescue
Seokju Lee, Francois Rameau, Sunghoon Im, In So Kweon
International Journal of Computer Vision (IJCV) 2022 / 07
660. PointMixer: MLP-Mixer for Point Cloud Understanding
Jaesung Choe*, Chunghyun Park*, Francois Rameau, Jaesik Park, In So Kweon
European Conference on Computer Vision (ECCV) 2022 / 10
659. Facial Depth and Normal Estimation using Single Dual-Pixel Camera
Minjun Kang, Jaesung Choe, Hyowon Ha, Hae-Gon Jeon, Sunghoon Im, In So Kweon, Kuk-Jin Yoon
European Conference on Computer Vision (ECCV) 2022 / 10
658. Fast Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Chaoning Zhang, Kang Zhang, Chenshuang Zhang, Axi Niu, Jiu Feng, Chang D. Yoo, In So Kweon
European Conference on Computer Vision (ECCV) 2022 / 10
657. ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Fei Pan, Sungsu Hur, Seokju Lee, Junsik Kim, In So Kweon
European Conference on Computer Vision (ECCV) 2022 / 10
656. A Unified Learning Framework for Large Vocabulary Video Object Detection
Sanghyun Woo, Kwanyong Park, Seoung Wug Oh, In So Kweon, Joon-Young Lee
European Conference on Computer Vision (ECCV) 2022 / 10
655. Tracking by Associating Clips
Sanghyun Woo, Kwanyong Park, Seoung Wug Oh, In So Kweon, Joon-Young Lee
European Conference on Computer Vision (ECCV) 2022 / 10
654. The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing
Dawit Mureja Argaw, Fabian Caba Heilbron, Markus Woodson, Joon-Young Lee, In So Kweon
European Conference on Computer Vision 2022 / 10
653. DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning
Ukcheol Shin, Kyunghyun Lee, In So Kweon
International Conference on Intelligent Robots and Systems (IROS) 2022 / 06
652. Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
Ukcheol Shin, Kyunghyun Lee, Byeong-Uk Lee, In So Kweon
IEEE Robotics and Automation Letters (RA-L) 2022 / 06
651. Self-supervised Depth and Ego-motion Estimation for Monocular Thermal Video using Multi-spectral Consistency Loss
Ukcheol Shin, Kyunghyun Lee, Seokju Lee, In So Kweon
IEEE Robotics and Automation Letters (RA-L) 2022 / 04
650. Identification of Vehicle Dynamics Model and Lever-arm for Arbitrarily Mounted Motion Sensor
Yoonjin Hwang, Yongseop Jeong, In So Kweon, Seibum Choi
IEEE Sensors 2021 / 12
649. Investigating Top-k White-Box and Transferable Black-box Attack
Chaoning Zhang, Philip Benz, Adil Karjauv, JaeWon Cho, Kang Zhang, In So Kweon
Computer Vision and Pattern Recognition, CVPR 2022 / 03
648. DASO: Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning
Youngtaek Oh, Dong-Jin Kim, and In So Kweon
Computer Vision and Pattern Recognition, CVPR 2022 / 03
647. Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Chaoning Zhang*, Kang Zhang,∗, Trung X. Pham,∗, Axi Niu, Zhinan Qiao Chang D. Yoo, In So Kweon
Computer Vision and Pattern Recognition, CVPR 2022 / 03
646. TubeFormer-DeepLab: Video Mask Transformer
Dahun Kim, Jun Xie, Huiyu Wang, Siyuan Qiao, Qihang Yu, Hong-Seok Kim,Hartwig Adam, In So Kweon, Liang-Chieh Chen
Computer Vision and Pattern Recognition, CVPR 2022 / 03
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