Chapter 12 showed the loss of map quality which arises as odometry errors accumulate and ARNE's position estimate becomes increasingly inaccurate. Chapter 9 presented a method by which ARNE can improve its position estimate by measuring the distance to known objects in its environment. The following sections describe the implementation of this localisation method and show the results of experiments to test its effectiveness.

A key component of the localisation method, the plant model, models the growth in positional uncertainty as ARNE moves. The plant model requires parameter values which are specific to the individual robot. Section 13.1 describes experiments to check that the parameters were approximately right for ARNE.

Section 13.2 then repeats the experiments from Chapter 12, but this time with the localization system in place, and compares the results. The loss of quality in the later stages of exploration is eliminated.

After the benefits of localisation have been demonstrated, Section 13.3 presents the results of wall-following with localisation in two other, more complicated, environments. The quality is shown to increase more slowly and to reach a lower maximum value in more cluttered environments. The reasons for this loss of quality are discussed.

The results of wall-following are then used to determine the best value for one of the central parameters of the map construction process, the confirmation threshold. Section 13.4 describes the experimental basis on which this choice is made.