2 code implementations • 22 Aug 2023 • Zhijian Qiao, Zehuan Yu, Binqian Jiang, Huan Yin, Shaojie Shen
Utilizing these GEMs, we present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR).
2 code implementations • 22 Jul 2023 • Zhijian Qiao, Zehuan Yu, Huan Yin, Shaojie Shen
In this paper, we propose a graph-theoretic framework to address the problem of global point cloud registration with low overlap.
1 code implementation • 30 Mar 2022 • Haozhe Wang, Zhiyang Liu, Lei Zhou, Huan Yin, Marcelo H Ang Jr
Vision-based grasp estimation is an essential part of robotic manipulation tasks in the real world.
no code implementations • 5 Jul 2021 • Zhiyi Lin, Chunyue Song, Jun Zhao, Chao Yang, Huan Yin
Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators.
no code implementations • 18 Jun 2021 • Huan Yin, Yue Wang, Rong Xiong
We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps.
1 code implementation • 30 Jan 2021 • Huan Yin, Xuecheng Xu, Yue Wang, Rong Xiong
Place recognition is critical for both offline mapping and online localization.
1 code implementation • 21 Oct 2020 • Xuecheng Xu, Huan Yin, Zexi Chen, Yue Wang, Rong Xiong
In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation.
1 code implementation • 15 Sep 2020 • Huan Yin, Runjian Chen, Yue Wang, Rong Xiong
In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping.
1 code implementation • 6 Dec 2017 • Huan Yin, Li Tang, Xiaqing Ding, Yue Wang, Rong Xiong
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge.