Zobrazeno 1 - 10
of 212
pro vyhledávání: '"Huang, Guoquan"'
Autor:
Merrill, Nathaniel, Huang, Guoquan
We present AB-VINS, a different kind of visual-inertial SLAM system. Unlike most VINS systems which only use hand-crafted techniques, AB-VINS makes use of three different deep networks. Instead of estimating sparse feature positions, AB-VINS only est
Externí odkaz:
http://arxiv.org/abs/2406.05969
While Global Navigation Satellite System (GNSS) is often used to provide global positioning if available, its intermittency and/or inaccuracy calls for fusion with other sensors. In this paper, we develop a novel GNSS-Visual-Inertial Navigation Syste
Externí odkaz:
http://arxiv.org/abs/2405.10874
Autor:
Yin, Peng, Jiao, Jianhao, Zhao, Shiqi, Xu, Lingyun, Huang, Guoquan, Choset, Howie, Scherer, Sebastian, Han, Jianda
In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a cornerstone technology. Despite the PR community's remarkable strides over th
Externí odkaz:
http://arxiv.org/abs/2405.04812
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
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
Autor:
Yin, Peng, Zhao, Shiqi, Cisneros, Ivan, Abuduweili, Abulikemu, Huang, Guoquan, Milford, Micheal, Liu, Changliu, Choset, Howie, Scherer, Sebastian
Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the la
Externí odkaz:
http://arxiv.org/abs/2209.04497
Publikováno v:
2022 TRO
Accurate and reliable sensor calibration is essential to fuse LiDAR and inertial measurements, which are usually available in robotic applications. In this paper, we propose a novel LiDAR-IMU calibration method within the continuous-time batch-optimi
Externí odkaz:
http://arxiv.org/abs/2205.03276
Autor:
Merrill, Nathaniel, Guo, Yuliang, Zuo, Xingxing, Huang, Xinyu, Leutenegger, Stefan, Peng, Xi, Ren, Liu, Huang, Guoquan
We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our knowledge, our system is among the first to utilize the camera pose inform
Externí odkaz:
http://arxiv.org/abs/2205.01823
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