In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map.
Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios.
To take the advantages of both, in this work, we present a complete visual inertial localization system based on a hybrid map representation to reduce the computational cost and increase the positioning accuracy.
This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs.
Incorporating prior structure information into the visual state estimation could generally improve the localization performance.
no code implementations • 16 Apr 2020 • Tianyu Liu, Qinghai Liao, Lu Gan, Fulong Ma, Jie Cheng, Xupeng Xie, Zhe Wang, Yingbing Chen, Yilong Zhu, Shuyang Zhang, Zhengyong Chen, Yang Liu, Meng Xie, Yang Yu, Zitong Guo, Guang Li, Peidong Yuan, Dong Han, Yuying Chen, Haoyang Ye, Jianhao Jiao, Peng Yun, Zhenhua Xu, Hengli Wang, Huaiyang Huang, Sukai Wang, Peide Cai, Yuxiang Sun, Yandong Liu, Lujia Wang, Ming Liu
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
The tracked points with and without the global planar information involve both global and local constraints of frames to the system.
In radio interferometry imaging, the gridding procedure of convolving visibilities with a chosen gridding function is necessary to transform visibility values into uniformly sampled grid points.
Instrumentation and Methods for Astrophysics
Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements.
End-to-end visual-based imitation learning has been widely applied in autonomous driving.
In this paper, we proposed a novel extrinsic calibration approach for the extrinsic calibration of range and image sensors.