Towards the exploitation of prior information in SLAM

Abstract

We consider how prior information can be exploited to improve the quality of SLAM. Prior information, such as aerial imagery, can be readily obtained for many environments. However, this information is often collected at a different time, using different sensors, different representations and from different vantage points than those used by the robot undertaking SLAM. In this paper, we describe a general probabilistic framework to overcome these difficulties. Our framework models the environment as a random set of latent structures which are observed by a set of sensing systems. Each sensing system gives rise to a different kind of map and, by associating features from the same structure across the different maps, parameterised constraints between the sets of features can be constructed. These parameterised constraints make it possible to transfer information between map representations. We demonstrate the use of the framework in a simulated environment to illustrate how geometric features of different dimensions can be fused together.