Loop closure detection in SLAM by combining visual CNN features and submaps

Abstract

Using simultaneous localization and mapping (SLAM) with 2D LIDAR is an efficient approach for robots to build a floor plan, but it is sensitive to the environment. For improving the accuracy, we match LIDAR data with sub-maps. Furthermore, we convert LIDAR data to images and merge with camera data for image matching. Combining the two approaches, we achieve robust and accurate loop closure detection. The descriptors generated by CNN model will be used as features to image matching for accuracy improvement.