  Deep learning-based image retrieval techniques for the loop closure detection
demonstrate satisfactory performance. However, it is still challenging to
achieve high-level performance based on previously trained models in different
geographical regions. This paper addresses the problem of their deployment with
simultaneous localization and mapping (SLAM) systems in the new environment.
The general baseline approach uses additional information, such as GPS,
sequential keyframes tracking, and re-training the whole environment to enhance
the recall rate. We propose a novel approach for improving image retrieval
based on previously trained models. We present an intelligent method, MAQBOOL,
to amplify the power of pre-trained models for better image recall and its
application to real-time multiagent SLAM systems. We achieve comparable image
retrieval results at a low descriptor dimension (512-D), compared to the high
descriptor dimension (4096-D) of state-of-the-art methods. We use spatial
information to improve the recall rate in image retrieval on pre-trained
models.
