Fleet Cleaner B.V. is a company that specializes in the cleaning of ship hulls of oceanic trade vessels using a mobile robot. The mobile robot mainly operates out of sight of the operator, making accurate localization of the robot crucial to its operation. However, the absence of absolute localization and sources for error build-up like wheel slip and sensor noise, increase the error between the estimated location and true location over time. Therefore, the goal of this thesis is to minimize the amount of error build-up between the estimated position and the true position of the robot, to allow more efficient operation of the robot and to ultimately allow the robot to operate autonomously.

The main sources for error build-up are determined by simulating the position estimator with modeled sensor- and perturbation models and evaluating their individual effect on the position estimator. Algorithms that combat the error build-up are established by synthesizing working principles from literature and extensively testing these principles using simulated data and finally verifying them using real but limited data.

The analysis of the sensor noise and perturbations revealed that the EKF is best suited to estimate the position of the robot and that the main sources for error build-up are wheel slip and IMU heading drift. The addition of a slip detection and velocity correction algorithm reduced the average error build-up per meter traveled by a factor of four. The addition of the heading correction algorithm reduced the error build-up by a factor of three and mitigates the need for intermittent resetting of the robot heading by the operator.

The velocity and heading correction algorithms improve the position estimator allowing the operator to more efficiently control the robot. In order to further reduce the error build-up, it is recommended to add two more wheel encoders to the robot. However, the error will still be unbounded, making the position estimator unsuitable for autonomous operation.