A control theoretic formulation of the generalized SLAM problem in robotics

Simultaneous localization and mapping (SLAM) has emerged as a key capability for autonomous mobile robots navigating in unknown environments. The basic idea behind SLAM is to concurrently obtain a map of the environment and an estimate of where the robot is placed within this map. In other words, the map and the robot´s pose have to be estimated at the same time, given the same data set. This paper revisits this problem from a control theoretic vantage point by reformulating the SLAM problem as a problem of simultaneously estimating the state and the output map of a controlled, dynamical system. What is different with this formulation is that the map is contained in the output map and not, as previously done, in the state of the system.