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pro vyhledávání: '"Geneva, Patrick"'
Robust multisensor fusion of multi-modal measurements such as IMUs, wheel encoders, cameras, LiDARs, and GPS holds great potential due to its innate ability to improve resilience to sensor failures and measurement outliers, thereby enabling robust au
Externí odkaz:
http://arxiv.org/abs/2309.15390
Autor:
Katragadda, Saimouli, Lee, Woosik, Peng, Yuxiang, Geneva, Patrick, Chen, Chuchu, Guo, Chao, Li, Mingyang, Huang, Guoquan
Achieving efficient and consistent localization a prior map remains challenging in robotics. Conventional keyframe-based approaches often suffers from sub-optimal viewpoints due to limited field of view (FOV) and/or constrained motion, thus degrading
Externí odkaz:
http://arxiv.org/abs/2309.09295
Publikováno v:
(International Journal of Robotics Research 2024)
In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and(or) rolling shu
Externí odkaz:
http://arxiv.org/abs/2308.05303
In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observability analysis for visual-inertial navigation systems (VINS) with ful
Externí odkaz:
http://arxiv.org/abs/2201.09170
In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loop-closure constraints across time and robots. In particular, we leverage covaria
Externí odkaz:
http://arxiv.org/abs/2103.12770
Autor:
Zuo, Xingxing, Yang, Yulin, Geneva, Patrick, Lv, Jiajun, Liu, Yong, Huang, Guoquan, Pollefeys, Marc
Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC
Externí odkaz:
http://arxiv.org/abs/2008.07196
As cameras and inertial sensors are becoming ubiquitous in mobile devices and robots, it holds great potential to design visual-inertial navigation systems (VINS) for efficient versatile 3D motion tracking which utilize any (multiple) available camer
Externí odkaz:
http://arxiv.org/abs/2006.15699
This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion
Externí odkaz:
http://arxiv.org/abs/1909.04102
Publikováno v:
2019 Conference on Computer Vision and Pattern Recognition (CVPR)
It holds great implications for practical applications to enable centimeter-accuracy positioning for mobile and wearable sensor systems. In this paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM algorithm, termed Schmidt-
Externí odkaz:
http://arxiv.org/abs/1903.08636
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