Efficient Topological Mapping with Image Sequence Partitioning

Topological maps are vital for fast and accurate localization in large environments. Sparse topological maps can be constructed by partitioning a sequence of images acquired by a robot, according to their appearance. All images in a partition have similar appearance and are represented by a node in a topological map. In this paper, we present a topological mapping framework which makes use of image sequence partitioning (ISP) to produce sparse maps. The framework facilitates coarse loop closure at node level and a finer loop closure at image level. Hierarchical inverted files (HIF) are proposed which are naturally adaptable to our sparse topological mapping framework and enable efficient loop closure. Computational gain attained in loop closure with HIF over sparse topological maps is demonstrated. Experiments are performed on outdoor environments using an omnidirectional camera.