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pro vyhledávání: '"Visual-Inertial SLAM"'
3D Gaussian Splatting (3DGS) has shown its ability in rapid rendering and high-fidelity mapping. In this paper, we introduce LVI-GS, a tightly-coupled LiDAR-Visual-Inertial mapping framework with 3DGS, which leverages the complementary characteristic
Externí odkaz:
http://arxiv.org/abs/2411.02703
Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. In agricultural applications, where environme
Externí odkaz:
http://arxiv.org/abs/2408.01716
Autonomous exploration of unknown space is an essential component for the deployment of mobile robots in the real world. Safe navigation is crucial for all robotics applications and requires accurate and consistent maps of the robot's surroundings. T
Externí odkaz:
http://arxiv.org/abs/2409.16972
We propose visual-inertial simultaneous localization and mapping that tightly couples sparse reprojection errors, inertial measurement unit pre-integrals, and relative pose factors with dense volumetric occupancy mapping. Hereby depth predictions fro
Externí odkaz:
http://arxiv.org/abs/2409.12051
Autor:
Joshi, Bharat, Rekleitis, Ioannis
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA) Proceedings of IEEE International Conference on Robotics and Automation (ICRA) 2024
This paper presents an extension to visual inertial odometry (VIO) by introducing tightly-coupled fusion of magnetometer measurements. A sliding window of keyframes is optimized by minimizing re-projection errors, relative inertial errors, and relati
Externí odkaz:
http://arxiv.org/abs/2409.09904
The traditional visual-inertial SLAM system often struggles with stability under low-light or motion-blur conditions, leading to potential lost of trajectory tracking. High accuracy and robustness are essential for the long-term and stable localizati
Externí odkaz:
http://arxiv.org/abs/2407.21348
Publikováno v:
Robotic Intelligence and Automation, 2024, Vol. 44, Issue 5, pp. 648-657.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RIA-06-2023-0081
Autor:
Merrill, Nathaniel, Huang, Guoquan
We present AB-VINS, a different kind of visual-inertial SLAM system. Unlike most popular VINS methods which only use hand-crafted techniques, AB-VINS makes use of three different deep neural networks. Instead of estimating sparse feature positions, A
Externí odkaz:
http://arxiv.org/abs/2406.05969
Akademický článek
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Autor:
Wang, Weihan, Chou, Chieh, Sevagamoorthy, Ganesh, Chen, Kevin, Chen, Zheng, Feng, Ziyue, Xia, Youjie, Cai, Feiyang, Xu, Yi, Mordohai, Philippos
We propose an accurate and robust initialization approach for stereo visual-inertial SLAM systems. Unlike the current state-of-the-art method, which heavily relies on the accuracy of a pure visual SLAM system to estimate inertial variables without up
Externí odkaz:
http://arxiv.org/abs/2403.07225