A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph

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. For example, the UAV may be shaken or experience a rapid drop in height due to sudden strong wind, which may in turn lead to lost tracking. What is more, when photographing a large area, the UAV flight path is usually planned in advance and the UAV does not generally return to the previously covered areas, so if the tracking fails during the flight, many areas of the map will be missing. To cope with the case of lost tracking, 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. We also tested the proposed method on both ground and indoor datasets, where it again showed a superior performance.