  Loop closure detection plays an important role in reducing localization drift
in Simultaneous Localization And Mapping (SLAM). It aims to find repetitive
scenes from historical data to reset localization. To tackle the loop closure
problem, existing methods often leverage on the matching of visual features,
which achieve good accuracy but require high computational resources. However,
feature point based methods ignore the patterns of image, i.e., the shape of
the objects as well as the distribution of objects in an image. It is believed
that this information is usually unique for a scene and can be utilized to
improve the performance of traditional loop closure detection methods. In this
paper we leverage and compress the information into a binary image to
accelerate an existing fast loop closure detection method via binary content.
The proposed method can greatly reduce the computational cost without
sacrificing recall rate. It consists of three parts: binary content
construction, fast image retrieval and precise loop closure detection. No
offline training is required. Our method is compared with the state-of-the-art
loop closure detection methods and the results show that it outperforms the
traditional methods at both recall rate and speed.
