Learning Visual-Inertial Odometry With Robocentric Iterated Extended Kalman Filter-Reference-Cited by-同舟云学术

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Learning Visual-Inertial Odometry With Robocentric Iterated Extended Kalman Filter

Published:2024 Issue: Volume:12 Page:109943-109956
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Duy Nguyen Khac¹^ORCID,Tuan Tran Dinh²^ORCID,Quyen Pham van³,Tuan Nguyen Dinh³^ORCID,Inoue Katsumi⁴,Lee Joo-Ho²,Quang Nguyen Anh³^ORCID

Affiliation:

1. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

2. College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan

3. School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

4. National Institute of Informatics, Chiyoda, Japan

Funder

Research Organization of Information and Systems (ROIS) National Institute of Informatics (NII) Open Collaborative Research 2023

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10630497.pdf?arnumber=10630497

Reference36 articles.

1. A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

2. Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

3. High-precision, consistent EKF-based visual-inertial odometry

4. Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

5. Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

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