论文标题
Visual Slam：当前的趋势是什么，期望什么？
Visual SLAM: What are the Current Trends and What to Expect?
论文作者
论文摘要
近年来，基于视觉的传感器显示出显着的性能，准确性和效率提高（SLAM）系统。在这方面，视觉同时定位和映射（VSLAM）方法是指采用相机进行姿势估计和地图生成的SLAM方法。我们可以看到许多研究作品证明VSLAM可以胜过传统方法，这些方法仅依赖于特定的传感器，例如LiDar，即使成本较低。 VSLAM方法利用不同的相机类型（例如，单眼，立体声和RGB-D）在各种数据集（例如Kitti，Tum RGB-D和Euroc）上进行了测试，在不同的环境（例如，室内和室外和室外）中，并在不同的环境中进行了测试，并采用了多种算法和方法论，以更好地了解环境。提到的变化使该主题对研究人员很受欢迎，并导致了广泛的VSLAM方法。在这方面，这项调查的主要目的是介绍VSLAM系统的最新进展，并讨论现有的挑战和趋势。我们已经对VSLAM领域发表的45篇有影响力的论文进行了深入的文献调查。我们已经通过不同的特征对这些手稿进行了分类，包括新颖性领域，目标，使用的算法和语义级别。我们还讨论了当前的趋势和未来方向，可以帮助研究人员调查它们。
Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. We can see many research works that demonstrated VSLAMs can outperform traditional methods, which rely only on a particular sensor, such as a Lidar, even with lower costs. VSLAM approaches utilize different camera types (e.g., monocular, stereo, and RGB-D), have been tested on various datasets (e.g., KITTI, TUM RGB-D, and EuRoC) and in dissimilar environments (e.g., indoors and outdoors), and employ multiple algorithms and methodologies to have a better understanding of the environment. The mentioned variations have made this topic popular for researchers and resulted in a wide range of VSLAMs methodologies. In this regard, the primary intent of this survey is to present the recent advances in VSLAM systems, along with discussing the existing challenges and trends. We have given an in-depth literature survey of forty-five impactful papers published in the domain of VSLAMs. We have classified these manuscripts by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. We also discuss the current trends and future directions that may help researchers investigate them.
