论文标题
基于子包和无方向连接的图的猛击地图恢复算法
A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph
论文作者
论文摘要
许多视觉同时定位和映射（SLAM）系统已被证明是准确且健壮的，并且在室内和地面数据集上具有实时性能功能。但是，在处理安装在无人机上（UAV）上的摄像机捕获的空中框架时，这些方法可能会出现问题，因为无人机的飞行高度可能难以控制，并且很容易受到环境的影响。这涉及跟踪线程在丢失跟踪之前检查随后的新帧与生成的地图之间的连接，从而继续在线工作。为了解决丢失的映射问题，这是许多应用程序中的问题，在丢失跟踪之后，基于单眼视觉大满贯，我们提出了一种通过相应的无方向连接的图表，通过顺序合并子封码来重建UAV数据集的完整全局映射。具体而言，从初始化过程到丢失跟踪的位置，并反复生成子链，并通过将这些子链视为节点和两个子链中的两个子链中的共同地图点来构建相应的无方向连接图。然后，通用地图点由词袋（BOW）方法确定，如果发现它们与Undirect Connected Graph中的在线地图连接，则将其合并。为了证明所提出的方法的性能，我们首先研究了无人机数据集上的性能，并且实验结果表明，在几个跟踪故障的情况下，映射的完整性明显好于当前主流大满贯方法的映射。
Many visual simultaneous localization and mapping (SLAM) systems have been shown to be accurate and robust, and have real-time performance capabilities on both indoor and ground datasets. However, these methods can be problematic when dealing with aerial frames captured by a camera mounted on an unmanned aerial vehicle (UAV) because the flight height of the UAV can be difficult to control and is easily affected by the environment.To cope with the case of lost tracking, many visual SLAM systems employ a relocalization strategy. This involves the tracking thread continuing the online working by inspecting the connections between the subsequent new frames and the generated map before the tracking was lost. To solve the missing map problem, which is an issue in many applications , after the tracking is lost, based on monocular visual SLAM, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph. Specifically, submaps are repeatedly generated, from the initialization process to the place where the tracking is lost, and a corresponding undirected connected graph is built by considering these submaps as nodes and the common map points within two submaps as edges. The common map points are then determined by the bag-of-words (BoW) method, and the submaps are merged if they are found to be connected with the online map in the undirect connected graph. To demonstrate the performance of the proposed method, we first investigated the performance on a UAV dataset, and the experimental results showed that, in the case of several tracking failures, the integrity of the mapping was significantly better than that of the current mainstream SLAM method.
