The startup company Fleet Cleaner has developed a mobile robot, specialized in the hull cleaning of large cargo vessels. Navigation and localization of this robot is currently performed manually. This is a difficult process that is greatly complicated during operation. This is mainly due to the availability of relative positioning sensors only, which are prone to error build-up and noise, and to the difficulty of interpreting optical underwater images in turbid water conditions. Instead, operators must rely on acoustic images from a forward-looking sonar. In the field of mobile robotics, Simultaneous Localization and Mapping (SLAM) is an often used technique to improve navigation and localization by utilizing visual information. The objective of this thesis is to develop a sonar-based SLAM framework, tailored to working environment of the Fleet Cleaner robot. The thesis scope has been restricted to the conceptual design of such a framework and the implementation of one of the subsystems, visual odometry. 
A conceptual design of a SLAM system is proposed using a systematic approach. Different working principles are evaluated according to operating conditions and requirements that specify desired behavior. Analysis of operating conditions reveal the limitations of sonar imagery, such as a high signal-to-noise ratio and inhomogeneous intensity patterns. In addition, the environment is sparse, with few distinct recognizable landmarks, limiting feature-based approaches. Because of these limitations, visual odometry is essential to reduce error build-up between loop closure corrections.
A Fourier-based approach to visual odometry is implemented, taking the whole image view into account instead of extracted features. By analyzing the dominant peak in the phase correlation matrix, the in-plane sonar motion between consecutive image frames can be estimated. Several image processing steps are necessary to improve peak sharpness, increasing the quality of registration.
To validate the proposed method, an experiment was conducted during cleaning of the Pioneering Spirit, the world’s largest construction vessel. Under normal circumstances, visual odometry showed less error build-up in the position estimate than wheel odometry. However, outliers appear when driving near the waterline, caused by reflections and wave reverberations. Ultimately, the proposed visual odometry method improves the current positioning system and serves as a basis for an integral SLAM implementation.

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.