Hierarchical visual mapping with omnidirectional images

A topological mapping framework designed for omnidirectional images is presented. Omnidirectional images acquired by the robot are organized as places which are represented as nodes in the topological graph/map. Places are regions in the environment over which the global scene appearance of all acquired images is consistent. A hierarchical loop closure algorithm is proposed which quickly sifts through the places to retrieve the most similar places and another level of thorough similarity analysis is performed over the images belonging to the retrieved places. An Image similarity metric based on spatial shift of local image features across omnidirectional/panoramic image pairs is proposed. Newly proposed VLAD (Vector of Locally Aggregated Descriptors) descriptors have been used for loop closure at place and image levels. Accuracy and efficiency of our system are corroborated with experimental results on three publicly available datasets. It is shown that our approach achieves good loop closure recall rates even without using epi-polar geometry verification common among many other approaches.