State of the Art

The SLAM Problem

Simultaneous Localization and Mapping (SLAM), also referred to as Concurrent Mapping and Localization (CML), is a fundamental problem in mobile robotics that has been the focus of substantial amount of research work in recent years [30, 2]. The objective of SLAM is to make it possible for a moving robot starting at an unknown location without previous knowledge of the environment to build a map using its onboard sensors while, at the same time, using this same map to compute the robot’s location. Although performing these two tasks simultaneously may seem complex, the essentials behind SLAM are indeed quite simple. Figure 2.1 illustrates the basics of the process. A moving vehicle will inherently accumulate errors in its position estimate as a consequence of the noise introduced in the deadreckoning and/or the inaccuracies in the use of a prediction model. Moreover, errors will also affect the map building process. The sensor that perceives the environment is mounted in the vehicle and therefore its position uncertainty will be incorporated when new information is added to the map. As a result, the vehicle will eventually get lost and the map will become unusable (Figure 2.1(a)). A system performing SLAM, however, is able to attenuate and even contain this uncertainty growth by means of the reiterated observation of the elements stored in the map. Figure 2.1(b) represents the situation where a new measurement from the robot is likely to correspond to an entity already incorporated in themap. Then, a data association process should be carried out to determine the correct matching. When this process is positive, this information is used to update the estimates of both the vehicle’s position and the map. Adding more information results in a better estimate and hence a reduction of the uncertainty in the problem (Figure 2.1(c)).