林辉灿, 吕强, 张洋, 马建业. 稀疏和稠密的VSLAM的研究进展[J]. 机器人, 2016, 38(5): 621-631. DOI: 10.13973/j.cnki.robot.2016.0621
引用本文: 林辉灿, 吕强, 张洋, 马建业. 稀疏和稠密的VSLAM的研究进展[J]. 机器人, 2016, 38(5): 621-631. DOI: 10.13973/j.cnki.robot.2016.0621
LIN Huican, LÜ Qiang, ZHANG Yang, MA Jianye. The Sparse and Dense VSLAM: A Survey[J]. ROBOT, 2016, 38(5): 621-631. DOI: 10.13973/j.cnki.robot.2016.0621
Citation: LIN Huican, LÜ Qiang, ZHANG Yang, MA Jianye. The Sparse and Dense VSLAM: A Survey[J]. ROBOT, 2016, 38(5): 621-631. DOI: 10.13973/j.cnki.robot.2016.0621

稀疏和稠密的VSLAM的研究进展

The Sparse and Dense VSLAM: A Survey

  • 摘要: 从稀疏的方法和稠密的方法两个方面对基于视觉的同时定位和地图构建(VSLAM)进行综述,重点阐述各个方面的关键技术和最新的研究进展,对比分析不同方法的优缺点和实现难点.介绍了深度学习技术应用到VSLAM领域的研究进展,并讨论了二者相互促进的结合方式.最后,展望了实时VSLAM的未来研究方向.

     

    Abstract: The sparse and dense approaches are two main aspects in vision-based simultaneous localization and mapping (VSLAM). Key technologies and latest research progress of the both approaches are reviewed in detail, and various aspects of the comparative advantages and disadvantages and the implementation difficulties of different methods are discussed. The research progress of deep learning techniques applied to VSLAM is reviewed, and the combination manner of the two approaches for improving performances is discussed. Finally, the future research directions of real-time VSLAM are explored.

     

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