Graph-based underwater localization techniques considering a rigorous on Lie group formulation

ENG- The main contributions of this thesis are two, properly presented in the four articles that form the articles compendium. The first one is the development of a scan matching algorithm based on Gaussian Mixture Models (GMMs) to represent the sensor noise projected to the scan that returns the uncertainty associated with the alignment result, an essential metric in SLAM problems. In addition, the Bayesian-GMM algorithm is first introduced to learn a GMM from a point cloud. The second main contribution of this thesis is the development of an algorithm to jointly preintegrate Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL) measurements to reach a tightly coupled estimation in an underwater Graph SLAM problem. Moreover, it allows compensating the preintegrated measurement from the Earth rotation rate measured by high grade IMUs. Both algorithms are formulated considering Lie algebra to properly manipulate robot states and sensor measurements, introducing the SEN (3) group to jointly preintegrate IMU and DVL measurements. Finally, this thesis also includes field experiments that prove the performance of the two proposed underwater navigators, one applying a Mechanical Profiling Sonar (MPS) and the other using a Mechanical Scanning MultiBeam EchoSounder (MS-MBES). The software architecture developed to implement both navigators is also presented, which provides a graph-based navigation framework to implement other navigators applying other sensor modalities