1 code implementation • 23 May 2023 • Xuecheng Xu, Yanmei Jiao, Sha Lu, Xiaqing Ding, Rong Xiong, Yue Wang
In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition.
no code implementations • 20 Oct 2022 • Sha Lu, Xuecheng Xu, Li Tang, Rong Xiong, Yue Wang
In recent years, deep learning brings improvements to place recognition by learnable feature extraction.
1 code implementation • 12 Oct 2022 • Xuecheng Xu, Sha Lu, Jun Wu, Haojian Lu, Qiuguo Zhu, Yiyi Liao, Rong Xiong, Yue Wang
In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multi-channel features.
no code implementations • 12 Jun 2022 • Zexi Chen, Yiyi Liao, Haozhe Du, Haodong Zhang, Xuecheng Xu, Haojian Lu, Rong Xiong, Yue Wang
Next, the rotation, scale, and translation are independently and efficiently estimated in the spectrum step-by-step using the DPC solver.
no code implementations • 2 Mar 2022 • Xiaqing Ding, Xuecheng Xu, Sha Lu, Yanmei Jiao, Mengwen Tan, Rong Xiong, Huanjun Deng, Mingyang Li, Yue Wang
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value.
no code implementations • 25 Sep 2021 • Zexi Chen, Haozhe Du, Xuecheng Xu, Rong Xiong, Yiyi Liao, Yue Wang
Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data.
1 code implementation • 7 Mar 2021 • Zexi Chen, Zheyuan Huang, Yunkai Wang, Xuecheng Xu, Yue Wang, Rong Xiong
In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all.
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 • 14 Dec 2020 • Yiyuan Pan, Xuecheng Xu, Xiaqing Ding, Shoudong Huang, Yue Wang, Rong Xiong
As a result, this deformable global dense map representation is able to keep the global consistency online.
no code implementations • 22 Nov 2020 • Yiyuan Pan, Xuecheng Xu, Weijie Li, Yunxiang Cui, Yue Wang, Rong Xiong
In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic representation, namely CORAL.
1 code implementation • 31 Oct 2020 • Zexi Chen, Jiaxin Guo, Xuecheng Xu, Yunkai Wang, Yue Wang, Rong Xiong
Utilizing the trained model under different conditions without data annotation is attractive for robot applications.
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.
2 code implementations • 21 Aug 2020 • Zexi Chen, Xuecheng Xu, Yue Wang, Rong Xiong
The crucial step for localization is to match the current observation to the map.