The mapping problem is a major player in mobile robotics, and it is essential for many real applications such as disaster response or nuclear decommissioning. Generally, the robotic mapping is addressed under the umbrella of simultaneous localization and mapping (SLAM). Several probabilistic techniques were developed in the literature to approach the SLAM problem, and despite the good performance, their convergence proof is only limited to linear Gaussian models. This thesis proposes an interval SLAM (i-SLAM) algorithm as a new approach that addresses the robotic mapping problem in the context of interval methods. The noise of the robot sensor is assumed bounded, and without any prior knowledge of its distribution, we specify soft conditions that guarantee the convergence of robotic mapping for the case of nonlinear models with non-Gaussian noise. A new theory about compact sets is developed in the context of real analysis to conclude such conditions. Then, a case study is presented where the performance of i-SLAM is compared to the probabilistic counterparts in terms of accuracy and efficiency. Moreover, this work presents an application for i-SLAM using an RGB-D sensor that operates in unknown environments. Interval methods and computer vision techniques are employed to extract planar landmarks in the environment. Then, a new hybrid data association approach is developed using a modified version of bag-of-features method to uniquely identify different landmarks across timesteps. Finally, the results obtained using the proposed data association approach are compared to the typical least-squares approaches, thus demonstrating the consistency and accuracy of the proposed approach.

Kelp beds support a vast and diverse ecosystem including marine mammals, fish, invertebrates, other algae and epibiota, yet these kelp beds can be highly ephemeral. Mapping the density and distribution of kelp beds, and assessing change over yearly cycles, are important objectives for coastal oceanography. However, nearshore habitat mapping is challenging, affected by dynamic currents, tides, shallow depths, frequent non-uniform obstacles and often turbid water. Noisy and often incomplete sensor data compound a lack of landmarks available for navigation. The intelligent, position-aware holonomic ROV (iROV) SeaBiscuit was designed specifically for this nearshore habitat mapping application and represents a novel synthesis of techniques and innovative solutions to nearshore habitat mapping. The concept of an iROV combines the benefits of autonomous underwater navigation and mapping while maintaining the flexibility and security of remote high-level control and supervision required for operation in hostile, complex underwater environments. An onboard battery provides an energy buffer for high-powered thrust and security of energy supply. Onboard low-level autonomy provides robust autopilot features, including station-keeping or course-holding in a flow, allowing the operator to direct the survey and supervise mapping data in realtime during acquisition. With the aim of providing high-usability maps on a budget feasible for small-scale field research groups, SeaBiscuit fuses the data from an orthogonal arrangement of a forward-facing multibeam sonar and a complementary 360° scanning sonar with a full navigation suite to explore and map the nearshore environment. Sensor fusion, coupled with the holonomic propulsion system, also allows optimal use of the information available from the limited budget sensor suite. Robust and reliable localisation is achieved even with noisy and incomplete sensor data using a relatively basic Inertial Navigation System and sonar-aided SLAM in the absence of an expensive Doppler velocity log or baseline navigation system. Holonomic motion in the horizontal plane and an axisymmetric hull provide the manoeuvrability required to operate in this complex environment, while allowing 3D maps to be generated in-transit. The navigation algorithms were tested mapping a piling dock and the habitat mapping sensors calibrated using an ‘artificial’ kelp bed of manually dimensioned kelp stipes transplanted to a sheltered but open-water real-world environment. Sea trials demonstrated mapping open ocean kelp beds, identifying clusters of stipes, converting this into a useful measure of biomass and generating a density surface across the kelp bed. This research provides field-proven techniques to improve the nearshore habitat mapping capabilities of underwater vehicles. Future work includes the transition to full-scale kelp bed mapping, and further development of the vehicle and sensor fusion algorithms to improve nearshore navigation.

This is a thesis on outdoor monocular visual SLAM in natural environments. The techniques proposed herein aim at estimating camera pose and 3D geometrical structure of the surrounding environment. This problem statement was motivated by the GPS-denied scenario for a sea-surface vehicle developed at Plymouth University named Springer. The algorithms proposed in this thesis are mainly adapted for the Springer’s environmental conditions, so that the vehicle can navigate on a vision based localization system when GPS is not available; such environments include estuarine areas, forests and the occasional semi-urban territories. The research objectives are constrained versions of the ever-abiding problems in the fields of multiple view geometry and mobile robotics. The research is proposing new techniques or improving existing ones for problems such as scene reconstruction, relative camera pose recovery and filtering, always in the context of the aforementioned landscapes (i.e., rivers, forests, etc.). Although visual tracking is paramount for the generation of data point correspondences, this thesis focuses primarily on the geometric aspect of the problem as well as with the probabilistic framework in which the optimization of pose and structure estimates takes place. Besides algorithms, the deliverables of this research should include the respective implementations and test data for these algorithms in the form of a software library and a dataset containing footage of estuarine regions taken from a boat, along with synchronized sensor logs. This thesis is not the final analysis on vision based navigation. It merely proposes various solutions for the localization problem of a vehicle navigating in natural environments either on land or on the surface of the water. Although these solutions can be used to provide position and orientation estimates when GPS is not available, they have limitations and there is still a vast new world of ideas to be explored.