By: Pau Vial Serrat
Supervised by: Dr. Marc Carreras Pérez / Dr. Narcís Palomeras Rovira
Abstract:
Locating an Autonomous Underwater Vehicle (AUV) is a complex problem since most of the common sensors applied in ground or aerial robotics experience a significant reduction in their capabilities underwater due to the strong attenuation of electromagnetic waves
within the water. Conventional underwater navigators use acoustic positioning systems that share information through water. However, the deployment of these systems is costly. A more inexpensive option is to fuse the AUV proprioceptive measurements with the measurements of the robot surroundings, solving the Simultaneous Localization and Mapping (SLAM) problem. Although during the last decades a great research effort has been made on this problem,
some fundamental questions concerning its formulation remain open. Aspects such as using Lie groups to model robot poses, applying a factor graph to model the joint probability distribution of the problem, developing an online solver algorithm, or reaching a tightly coupled
estimation have not found a consensus until recent years.
To enhance the surveying capabilities of an AUV, this thesis focuses on the development of a state-of-the-art AUV navigator, transferring recent findings from generalist robotics to the underwater field. Thus, a tightly coupled and graph-based underwater navigator is proposed that properly applies Lie algebra when necessary. The navigator is based on acoustic range sensors that provide point cloud measurements of the robot’s surroundings.
The main contributions of this thesis are two, properly presented in the four articles that form this document. 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 graphbased navigation framework to implement other navigators applying other sensor modalities.
