Zobrazeno 1 - 10
of 500
pro vyhledávání: '"Huang, Xinming"'
This paper presents a novel and fast approach for ground plane segmentation in a LiDAR point cloud, specifically optimized for processing speed and hardware efficiency on FPGA hardware platforms. Our approach leverages a channel-based segmentation me
Externí odkaz:
http://arxiv.org/abs/2408.10410
In this study, we introduce a novel parallel processing framework for real-time point cloud ground segmentation on FPGA platforms, aimed at adapting LiDAR algorithms to the evolving landscape from mechanical to solid-state LiDAR (SSL) technologies. F
Externí odkaz:
http://arxiv.org/abs/2408.10404
Autor:
Ye, Changqing, Liu, Beige, Cao, Zhe, Han, Lingzhi, Huang, Xinming, Jiang, Min, Liu, Dong, Lin, Qing, Wan, Shitian, Wu, Yusheng, Zhao, Lei, Zhang, Yue, Peng, Xinhua, Zhao, Zhengguo
The investigation of beyond-Standard-Model particles is a compelling direction in the pursuit of new physics. One such hypothetical particle, the magnetic monopole, has garnered considerable attention due to its strong theoretical motivation and pote
Externí odkaz:
http://arxiv.org/abs/2406.12379
Real-time perception and motion planning are two crucial tasks for autonomous driving. While there are many research works focused on improving the performance of perception and motion planning individually, it is still not clear how a perception err
Externí odkaz:
http://arxiv.org/abs/2305.06966
The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs have large distortions. To address this challenge, in this paper we prop
Externí odkaz:
http://arxiv.org/abs/2303.11526
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the sim
Externí odkaz:
http://arxiv.org/abs/2303.11169
Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural
Externí odkaz:
http://arxiv.org/abs/2207.05888
Publikováno v:
In Journal of Financial Markets September 2024 70
LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise and efficient. Due to its non-convexity and its local iterative strate
Externí odkaz:
http://arxiv.org/abs/2110.10194
Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. To meet the real-time requirement, existing research proposed to apply the connected-component-labeling (CCL) technique
Externí odkaz:
http://arxiv.org/abs/2109.08224