Mosaicing Large Cyclic Environments for Visual Navigation in Autonomous Vehicles

Abstract

Mobile robot localization from large-scale appearance mosaics has been showing increasing promise as a low-cost, high-performance and infrastructure-free solution to vehicle guidance in man-made environments. The feasibility of this technique relies on the construction of a locally smooth and globally consistent high-resolution mosaic of the vehicle? environment, efficiently done using observations that have low spatial and temporal persistence. The problem of loop closure in cyclic environments that plagues this process is one that is commonly encountered in all map-building procedures, and its solution is often computationally expensive. This paper presents a method that reliably generates consistent maps at low computational cost, while fully exploiting the topology of the observations. Extensions to a real-time implementation are discussed along with results using simulated data and those from real indoor environments.