Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Xu Xuecheng"'
Global localization using onboard perception sensors, such as cameras and LiDARs, is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most approaches achieve global localization by sequential place recognition
Externí odkaz:
http://arxiv.org/abs/2409.00206
This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation
Externí odkaz:
http://arxiv.org/abs/2305.13814
Autor:
Yin, Huan, Xu, Xuecheng, Lu, Sha, Chen, Xieyuanli, Xiong, Rong, Shen, Shaojie, Stachniss, Cyrill, Wang, Yue
Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and map
Externí odkaz:
http://arxiv.org/abs/2302.07433
LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visi
Externí odkaz:
http://arxiv.org/abs/2210.11029
Global localization plays a critical role in many robot applications. LiDAR-based global localization draws the community's focus with its robustness against illumination and seasonal changes. To further improve the localization under large viewpoint
Externí odkaz:
http://arxiv.org/abs/2210.05984
Autor:
Chen, Zexi, Liao, Yiyi, Du, Haozhe, Zhang, Haodong, Xu, Xuecheng, Lu, Haojian, Xiong, Rong, Wang, Yue
Pose registration is critical in vision and robotics. This paper focuses on the challenging task of initialization-free pose registration up to 7DoF for homogeneous and heterogeneous measurements. While recent learning-based methods show promise usin
Externí odkaz:
http://arxiv.org/abs/2206.05707
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of
Externí odkaz:
http://arxiv.org/abs/2204.07992
Autor:
Ding, Xiaqing, Xu, Xuecheng, Lu, Sha, Jiao, Yanmei, Tan, Mengwen, Xiong, Rong, Deng, Huanjun, Li, Mingyang, Wang, Yue
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid of gravity alignment, the degree of freedom in point cloud registrati
Externí odkaz:
http://arxiv.org/abs/2203.00924
Autor:
Xu, Xuecheng1 (AUTHOR), Yuan, Qihan1 (AUTHOR), Xu, Linlin1 (AUTHOR), Hu, Mingchang1 (AUTHOR), Xu, Jidong1 (AUTHOR), Wang, Yuanfei2 (AUTHOR), Song, Yu3 (AUTHOR) bmusy2002sy@163.com
Publikováno v:
PLoS ONE. 5/23/2024, Vol. 19 Issue 5, p1-17. 17p.
Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While being fully interpretable, they rely on manual interference and empiri
Externí odkaz:
http://arxiv.org/abs/2109.12292