Simultaneous Localization and Mapping

Abstract

This papers provides two contributions to the problem of Simultaneous Localization and Mapping (SLAM): First we discuss properties of the problem itself and of the intended semantics of an uncertain map representation, with the main idea of "representing certainty of relations despite the uncertainty of positions". We propose some requirements an ideal solution of SLAM should have concerning uncertainty, memory space and computation time and discuss existing approaches in the light of these requirements. The second part proposes a representation based on sparse information matrices together with some properties that motivate this approach. This is shown to comply to the uncertainty and space requirements. To derive an estimated map from the representation a sparse linear equation system has to be solved. However, an update of the representation itself needs only constant time, making it highly attractive for building a SLAM algorithm.