融合多粒度特征的低资源语言词性标记和依存分析联合模型(A Joint Model with Multi-Granularity Features of Low-resource Language POS Tagging and Dependency Parsing)

no code implementations • CCL 2021 • Sha Lu, Cunli Mao, Zhengtao Yu, Chengxiang Gao, Yuxin Huang, Zhenhan Wang

Dependency Parsing POS +1

Leveraging BEV Representation for 360-degree Visual Place Recognition

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.

Sensor Fusion Visual Place Recognition

DeepRING: Learning Roto-translation Invariant Representation for LiDAR based 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.

Loop Closure Detection One-Shot Learning +1

RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map

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.

Translation

Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud

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.

Point Cloud Registration Translation

Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation

no code implementations • 13 Nov 2020 • Sha Lu, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu, Jiuyong Li

Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights.

feature selection Unsupervised Anomaly Detection