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dynamic slam Visual Odometry Based on Instance Segmentation:A Comprehensive Review

Computers, Materials & Continua 2024年第1期78卷 167-196页

作者： Jiansheng Peng Qing Yang Dunhua Chen Chengjun Yang Yong Xu Yong Qin College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

dynamic Simultaneous Localization and Mapping(slam)in visual scenes is currently a major research area in fields such as robot navigation and autonomous ***,in the face of complex real-world envi-ronments,current dynamic slam systems struggle to achieve precise localization and map *** the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic slam visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic *** to dynamic slam systems based on deep learning methods such as object detection and semantic segmentation,dynamic slam systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the slam system’s *** article not only introduces traditional dynamic slam systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic slam systems based on instance segmentation,comparing and summarizing their advantages and *** comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic ***,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic slam *** recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic slam systems based on instance ***,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

关键词： dynamic slam instance segmentation visual odometry

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Clusterslam:A slam backend for simultaneous rigid body clustering and motion estimation

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Computational Visual Media 2021年第1期7卷 87-101页

作者： Jiahui Huang Sheng Yang Zishuo Zhao Yu-Kun Lai Shi-Min Hu BNRist Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Alibaba A.I.Labs Hangzhou 311121China School of Computer Science and Informatics Cardiff UniversityCardiffCF243AAUK

We present a practical backend for stereo visual slam which can simultaneously discover individual rigid bodies and compute their motions in dynamic *** recent factor graph based state optimization algorithms have shown their ability to robustly solve slam problems by treating dynamic objects as outliers,their dynamic motions are rarely *** this paper,we exploit the consensus of 3 D motions for landmarks extracted from the same rigid body for clustering,and to identify static and dynamic objects in a unified ***,our algorithm builds a noise-aware motion affinity matrix from landmarks,and uses agglomerative clustering to distinguish rigid *** decoupled factor graph optimization to revise their shapes and trajectories,we obtain an iterative scheme to update both cluster assignments and motion estimation *** on both synthetic scenes and KITTI demonstrate the capability of our approach,and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

关键词： dynamic slam motion segmentation scene perception

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A Monocular slam System with Mask Loop Closing

A Monocular SLAM System with Mask Loop Closing

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第32届中国控制与决策会议

作者： Bo Han Li Xu College of Electrical Engineering Zhejiang University Robotics Institute of Zhejiang University

In recent years,monocular simultaneous localization and mapping（slam） algorithms have achieved good performance,while most of them could not deal with dynamic *** paper proposes a novel monocular slam system with mask loop closing（MLC-slam） to deal with dynamic *** proposed system combines the direct and feature-based methods to track fast and *** proposed approach achieves motion refinement and loop closing in the feature-based module,which exploits semantic information by training a visual vocabulary offline with semantic labels from the mask *** on the TUM mono VO dataset shows that MLC-slam achieves better performance than the state-of-the-art monocular slam algorithms:DSO and ORB-slam,in terms of accuracy,robustness,and the performance of loop closing.

关键词： Monocular slam Loop closing dynamic slam

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