Skip to main navigation Skip to search Skip to main content

USP-SLAM: Deep Learning Based Visual SLAM with Robust Feature Extraction under Dynamic Environments

  • Ziqi Li
  • , Wei Gao*
  • , Haoyao Chen
  • , Shiwu Zhang*
  • *Corresponding author for this work
  • University of Science and Technology of China
  • Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The majority of visual SLAM systems that rely on feature point methods assume that scenes are static. Consequently, addressing the interference caused by dynamic objects in reality poses a persistent challenge. This paper proposes a visual semantic SLAM system, USP-SLAM, to address this challenge. It takes advantage of a lightweight feature extraction network, modified based on the Superpoint network, to realize robust feature extraction even with images of low illumination and rapid viewpoint changes. Moreover, a modified version of the Unet network is incorporated into USP-SLAM to achieve accurate semantic segmentation of dynamic objects. Combining these two networks, the dynamic point removal module within USP-SLAM uses the optical flow method to achieve satisfying visual SLAM performance. The experimental results on the publicly available TUM RGB-D dataset show that for dynamic scene sequences, USP-SLAM improves the absolute trajectory error by 96.12% compared to its reference system ORBSLAM2. Besides, USP-SLAM also outperforms in non-dynamic scenarios, demonstrating the superiority of its system design.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023
EditorsMehmet Dogar, Bin Fang, Dimitrios Kanoulas, Jia Pan, Alessandra Sciutti, Moju Zhao, Guanjun Bao, Bimbo Joao, Boyle Jordan Hylke, He Chen, Chen Teng, Yunduan Cui, Dagnino Giulio, Wenbo Ding, Liang Du, Farinha Andre, Yuan Gao, Hasegawa Shun, Liang He, Taogang Hou, Zhe Hu, Zhong Huang, Jackson-Mills George, Yunfeng Ji, Jirak Doreen, Feng Ju, Kaddouh Bilal, Kim Wansoo, Takuya Kiyokawa, Haiyuan Li, Peng Li, Shihao Li, Xu Li, Jianfeng Liao, Ling Jie, Chunfang Liu, Quanquan Liu, Liang Lu, Qiuyue Luo, Yudong Luo, Zebing Mao, Martinez-Hernandez Uriel, Matsuno Takahiro, Nguyen Thanh Luan, Nishio Takuzumi, Pasquali Dario, Pierella Camilla, Chao Ren, Ricci Serena, Rossini Luca, Shi Fan, Summa Susanna, Rongchuan Sun, Zhenglong Sun, Vannucci Fabio, Gang Wang, Wei Wang, Xin Wang, Yuquan Wang, Ziya Wang, Qingxiang Wu, Xiaojun Wu, Yuxin Sun, Youcan Yan, Lei Yang, Yanokura Iori, Jingfan Zhang, Shuai Zhang, Tianwei Zhang, Jinglei Zhao, Na Zhao, Chengxu Zhou, Peng Zhou, Haifei Zhu
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350325706
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023 - Koh�Samui, Thailand
Duration: 4 Dec 20239 Dec 2023

Publication series

Name2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

Conference

Conference2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023
Country/TerritoryThailand
CityKoh�Samui
Period4/12/239/12/23

Fingerprint

Dive into the research topics of 'USP-SLAM: Deep Learning Based Visual SLAM with Robust Feature Extraction under Dynamic Environments'. Together they form a unique fingerprint.

Cite this