Search Results for author: Jinyu Miao

Found 6 papers, 2 papers with code

Poses as Queries: Image-to-LiDAR Map Localization with Transformers

no code implementations7 May 2023 Jinyu Miao, Kun Jiang, Yunlong Wang, Tuopu Wen, Zhongyang Xiao, Zheng Fu, Mengmeng Yang, Maolin Liu, Diange Yang

High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks.

Autonomous Driving

ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction

2 code implementations6 Dec 2021 Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter C. Y. Chen, Zhengguo Li

The reprojection loss is then proposed to directly optimize these sub-pixel keypoints, and the dispersity peak loss is presented for accurate keypoints regularization.

Homography Estimation Keypoint Detection +1

Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection

no code implementations30 Jul 2021 Haosong Yue, Jinyu Miao, Weihai Chen, Wei Wang, Fanghong Guo, Zhengguo Li

Localizing pre-visited places during long-term simultaneous localization and mapping, i. e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies.

Loop Closure Detection Simultaneous Localization and Mapping

Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments

no code implementations18 Mar 2021 Yuxin Tian, Jinyu Miao, Xingming Wu, Haosong Yue, Zhong Liu, Weihai Chen

In this paper, we address the challenges of place recognition due to dynamics and confusable patterns by proposing a discriminative and semantic feature selection network, dubbed as DSFeat.

feature selection Visual Place Recognition

RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization

1 code implementation1 Dec 2020 Dongjiang Li, Jinyu Miao, Xuesong Shi, Yuxin Tian, Qiwei Long, Tianyu Cai, Ping Guo, Hongfei Yu, Wei Yang, Haosong Yue, Qi Wei, Fei Qiao

Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.

Indoor Localization Visual Localization

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