Simultaneous Localization and Mapping

Abstract

This article provides a comprehensive introduction into the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways. One might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robot's location. SLAM servers both of these purposes. We review three major paradigms of algorithms from which a huge number of recently published methods are derived. First comes the traditional approach, which relies in the extended Kalman filter (EKF) for representing the robot's best estimate. The second paradigm draws its intuition from the fact that the SLAM problem can be viewed as a sparse graph of constraints, and it applies nonlinear optimization for recovering the map and the robot's locations. Finally, we survey the particle filter paradigm, which applies non-parametric density estimation and efficient factorization methods to the SLAM problem. This article discusses extensions of these basic methods. 11, elucidates variants of the SLAM problem and poses a taxonomy for the field. Relevant research is referenced extensively, and open research problems are discussed.