Zobrazeno 1 - 10
of 815
pro vyhledávání: '"Visual-inertial odometry"'
Autor:
Khac Duy Nguyen, Dinh Tuan Tran, van Quyen Pham, Dinh Tuan Nguyen, Katsumi Inoue, Joo-Ho Lee, Anh Quang Nguyen
Publikováno v:
IEEE Access, Vol 12, Pp 109943-109956 (2024)
In recent years, deep learning methodologies have been increasingly applied to the intricate challenges of visual-inertial odometry (VIO), especially in scenarios with rapid movements and scenes lacking clear structure. This paper introduces a novel
Externí odkaz:
https://doaj.org/article/412c36b9f99b4b2aa138b48ac49600af
Publikováno v:
Sensors, Vol 24, Iss 22, p 7297 (2024)
Simultaneous Localization And Mapping (SLAM) algorithms play a critical role in autonomous exploration tasks requiring mobile robots to autonomously explore and gather information in unknown or hazardous environments where human access may be difficu
Externí odkaz:
https://doaj.org/article/3c851097856940de8bc9fff3cb74b2b3
Publikováno v:
Sensors, Vol 24, Iss 20, p 6665 (2024)
Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measu
Externí odkaz:
https://doaj.org/article/281c7dbbb63040a394c0e2d56f474263
Publikováno v:
Drones, Vol 8, Iss 9, p 487 (2024)
In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), camera
Externí odkaz:
https://doaj.org/article/e9914c8a4d214211881de4312675ce62
Publikováno v:
Remote Sensing, Vol 16, Iss 16, p 2970 (2024)
Due to the limitation of a single sensor such as only camera or only LiDAR, the Visual SLAM detects few effective features in the case of poor lighting or no texture. The LiDAR SLAM will also degrade in an unstructured environment and open spaces, wh
Externí odkaz:
https://doaj.org/article/a63b5a1c555944e988bbe8996800cfc3
Publikováno v:
Sensors, Vol 24, Iss 16, p 5218 (2024)
In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking a
Externí odkaz:
https://doaj.org/article/1a542a9cd552436e9663d0d3ef6b0af5
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 3, Pp 101982- (2024)
In collaborative missions, it is crucial for each agent to have a clear understanding of the status among others. Therefore, various related works have been conducted, including virtual reality interactions and real-life swarm jobs. However, these ty
Externí odkaz:
https://doaj.org/article/415036a478784283b7d633743f87b348
Publikováno v:
Satellite Navigation, Vol 4, Iss 1, Pp 1-15 (2023)
Abstract Visual-Inertial Odometry (VIO) has been developed from Simultaneous Localization and Mapping (SLAM) as a low-cost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning. However, VIOs usually ha
Externí odkaz:
https://doaj.org/article/5561afceb3b94391afe92c168170c739
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 3895-3905 (2023)
This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to
Externí odkaz:
https://doaj.org/article/ff4be8cf6fcd41c699d7afa96632eb9f
Autor:
Xiang-Shi Tang, Teng-Hu Cheng
Publikováno v:
IEEE Access, Vol 11, Pp 104028-104037 (2023)
The development of a new system called Flexible Lidar-Visual-Inertial Odometry (F-LVINS) offers improved localization accuracy even in challenging environments. This system employs a multi-sensor fusion approach that is flexible and adapts to changes
Externí odkaz:
https://doaj.org/article/81df3285192a41d698548b379ff92986