A Precise and Real-Time Loop-closure Detection for SLAM Using the RSOM Tree

Abstract

In robotic applications of visual simultaneous localization and mapping (SLAM) techniques, loop-closure detection detects whether or not a current location has previously been visited. We present an online and incremental approach to detect loops when images come from an already visited scene and learn new information from the environment. Instead of utilizing a bag-of-words model, the attributed graph model is applied to represent images and measure the similarity between pairs of images in our method. In order to position a camera in visual environments in real-time, the method demands retrieval of images from the database through a clustering tree that we call RSOM (recursive self-organizing feature map). As long as the match is found between the current graph and several graphs in the database, a threshold will be chosen to judge whether loop-closure is accepted or rejected. The results demonstrate the method's accuracy and real-time performance by testing several videos collected from a digital camera fixed on vehicles in indoor and outdoor environments.