  Visual simultaneous localization and mapping (vSLAM) and 3D reconstruction
methods have gone through impressive progress. These methods are very promising
for autonomous vehicle and consumer robot applications because they can map
large-scale environments such as cities and indoor environments without the
need for much human effort. However, when it comes to loop detection and
optimization, there is still room for improvement. vSLAM systems tend to add
the loops very conservatively to reduce the severe influence of the false
loops. These conservative checks usually lead to correct loops rejected, thus
decrease performance. In this paper, an algorithm that can sift and majorize
loop detections is proposed. Our proposed algorithm can compare the usefulness
and effectiveness of different loops with the dense map posterior (DMP) metric.
The algorithm tests and decides the acceptance of each loop without a single
user-defined threshold. Thus it is adaptive to different data conditions. The
proposed method is general and agnostic to sensor type (as long as depth or
LiDAR reading presents), loop detection, and optimization methods. Neither does
it require a specific type of SLAM system. Thus it has great potential to be
applied to various application scenarios. Experiments are conducted on public
datasets. Results show that the proposed method outperforms state-of-the-art
methods.

Computer Vision and Pattern Recognition