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基于多机器人的协同VSLAM综述
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南京信息工程大学


A review of VSLAM based on multiple robots
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Affiliation:

Nanjing University of Information Science and Technology

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    摘要:

    面对大规模环境建图要求时，通过使用轻便的机器人群去感知环境，采用多机器人协同SLAM（同步定位与地图构建）方案可以解决在单个机器人SLAM方案下面临的个体成本高昂、全局误差累积、计算量和风险过于集中的问题，有着极强的鲁棒性与稳定性。本文回顾了多机器人协同SLAM的发展历史，介绍了相关的融合算法与融合架构，并从机器学习分类的角度梳理了现有的协同SLAM算法；同时还介绍了未来多机器人SLAM发展的重要方向：深度学习、语义地图与多机器人VSLAM的结合问题，并对未来发展做出了展望。

    Abstract:

    The multi-robot collaborative SLAM scheme solves the issues of high individual cost, global error accumulation, excessive concentration of calculation and risk under a single robot SLAM(Simultaneous Localization and Mapping) scheme, and has strong robustness and stability, which is a research hotspot when it comes to large-scale environmental mapping requirements. This study analyzes the history of this field's growth and introduces the multi-robot collaborative SLAM's fusion method and architecture. The current collaborative SLAM methods are arranged from the viewpoint of machine learning classification. The future development of deep learning, semantic maps, and multi-robot VSLAM, along with other significant multi-robot SLAM development directions, are also introduced at this time.

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王曦杨,陈炜峰,尚光涛,周铖君,李振雄,徐崇辉.基于多机器人的协同VSLAM综述[J].南京信息工程大学学报,,():

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  • 收稿日期:2023-04-04
  • 最后修改日期:2023-06-12
  • 录用日期:2023-06-13

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