Multi-resolution map building and loop closure with omnidirectional images

Abstract

A topological mapping approach for omnidirectional images capable of answering loop closure queries at multiple resolutions is presented. The environment is mapped hierarchically using two layers. The first layer consists of individual images and the second layer represents regions of the environment composed of groups of images from the first layer. A hierarchical algorithm is formulated that exploits this map structure for an efficient and accurate loop closure without the need of geometric verification. The vital parameters of loop closure are automatically learned from training data. Performance of our loop closure algorithm is experimentally evaluated on various publicly available datasets and compared to two state of the art techniques. The results show that agreeable performance is achieved even on low quality datasets without the need for geometric verification of loop closures common among many contemporary approaches.