  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.
