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A Review of Visual SLAM Based on Unmanned Systems

机译：基于无人系统的视觉SLAM综述

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Simultaneous Localization and Mapping (SLAM) consists of the immediate construction of the environment and the state estimation of the robot in it, while Visual SLAM (VSLAM) is the use of cameras and other visual sensors for SLAM. VSLAM has become an important part of mobile robots, drones, unmanned vehicles and other unmanned systems in unknown environments to achieve full-scale navigation and environmental perception. First, the principle of architecture, the mathematical models, the current research status and the algorithms of each part have been reviewed. Then, the research hotspots and current facing challenges on VSLAM were summarized from three parts: (i) VSLAM and deep learning; (ii) data processing of multi-sensor; (iii) VSLAM in visual/inertial navigation. Moreover, the research trend of VSLAM were further analyzed, including (i) deep learning and deep estimation, (ii) active and multi-robot VSLAM and (iii) semantic VSLAM. At last, the future development of VSLAM was discussed, which may provide a certain guiding significance for researchers in this area.
机译：同时定位和映射（SLAM）包括环境的即时构建和机器人在其中的状态估计，而视觉SLAM（VSLAM）则是使用摄像机和其他视觉传感器进行SLAM。VSLAM已经成为移动机器人、无人机、无人机和其他未知环境中无人系统的重要组成部分，以实现全方位导航和环境感知。首先，回顾了体系结构的原理、数学模型、研究现状以及各部分的算法。然后，从三个方面总结了VSLAM的研究热点和当前面临的挑战：（1）VSLAM与深度学习；（二）多传感器数据处理；（iii）视觉/惯性导航中的VSLAM。进一步分析了VSLAM的研究趋势，包括（i）深度学习和深度估计，（ii）主动多机器人VSLAM和（iii）语义VSLAM。最后，对VSLAM的未来发展进行了讨论，对该领域的研究人员具有一定的指导意义。

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