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pro vyhledávání: '"Song, Shuangfu"'
Autor:
Huang, Kai, Zhao, Junqiao, Lin, Jiaye, Zhu, Zhongyang, Song, Shuangfu, Ye, Chen, Feng, Tiantian
Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to ra
Externí odkaz:
http://arxiv.org/abs/2405.01316
Accurate and dense mapping in large-scale environments is essential for various robot applications. Recently, implicit neural signed distance fields (SDFs) have shown promising advances in this task. However, most existing approaches employ projectiv
Externí odkaz:
http://arxiv.org/abs/2401.03412
The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale
Externí odkaz:
http://arxiv.org/abs/2202.04816
Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robot's visual perception. We use a joint self-supervised method to estimate the three geometric elements. Depth network, optical flo
Externí odkaz:
http://arxiv.org/abs/2105.14520
Akademický článek
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Autor:
Osten, Wolfgang, Nikolaev, Dmitry P., Zhou, Jianhong, Li, Jianfeng, Zhao, Junqiao, Song, Shuangfu, Feng, Tiantian
Publikováno v:
Proceedings of SPIE; January 2021, Vol. 11605 Issue: 1 p116050T-116050T-8, 1044459p
Autor:
Osten, Wolfgang, Nikolaev, Dmitry P., Zhou, Jianhong, Li, Jianfeng, Zhao, Junqiao, Song, Shuangfu, Feng, Tiantian
Publikováno v:
Proceedings of SPIE; 10/29/2020, Vol. 11605, p116050T-116050T-8, 1p