Search Results for author: Xuesong Shi

Found 7 papers, 3 papers with code

Hierarchical Segment-based Optimization for SLAM

no code implementations7 Nov 2021 Yuxin Tian, Yujie Wang, Ming Ouyang, Xuesong Shi

This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system.

Frame Simultaneous Localization and Mapping

Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations

no code implementations ICCV 2021 Zike Yan, Yuxin Tian, Xuesong Shi, Ping Guo, Peng Wang, Hongbin Zha

We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation.

Continual Learning

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

DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features

3 code implementations12 Aug 2020 Dongjiang Li, Xuesong Shi, Qiwei Long, Shenghui Liu, Wei Yang, Fangshi Wang, Qi Wei, Fei Qiao

For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments.

Loop Closure Detection Simultaneous Localization and Mapping

OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning

2 code implementations15 Nov 2019 Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, Fei Qiao, Rosa H. M. Chan

Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks.

Object Recognition

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