Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Robust state estimation (Sensor fusion)

This research is the follow up of Agostino Martinelli's investigations carried out during the last five years, which are in the framework of the visual and inertial sensor fusion problem and the unknown input observability problem.

Visual-inertial structure from motion

Participant : Agostino Martinelli.

We have continued our study on the visual inertial sensor fusion problem in the cooperative case, with a special focus on the case of two agents. During this year, we have carried out an exhaustive analysis of all the singularities and minimal cases of this cooperative sensor fusion problem. As in the case of a single agent and in the case of other computer vision problems, the key of the analysis is the establishment of an equivalence between the cooperative visual-inertial sensor fusion problem and a Polynomial Equation System (PES). In the case of a single agent, the PES consists of linear equations and a single polynomial of second degree. In the case of two agents, the number of second degree equations becomes three and, also in this case, a complete analytic solution can be obtained [19], [20]. The power of the analytic solution is twofold. From one side, it allows us to determine the state without the need of an initialization. From another side, it provides fundamental insights into all the structural properties of the problem. The research of this year has focused on this latter issue. Specifically, we have obtained all the minimal cases and singularities depending on the number of camera images and the relative trajectory between the agents. The problem, when non singular, can have up to eight distinct solutions. The usefulness of this analysis has also been illustrated with simulations. In particular, we have quantitatively obtained how the performance of the state estimation worsens near a singularity. The results of this research will be published by the Robotics and Automation Letter (RA-L) journal [18].

Unknown Input Observability

Participant : Agostino Martinelli.

The Unknown Input Observability problem (UIO) in the nonlinear case was an open problem since the sixties years, when it was solved only in the linear case. In the last five years, I have obtained its general analytic solution. So far, I only published the solution for systems characterized by driftless dynamics. In particular, this solution was published as a full paper on the IEEE Transaction on Automatic Control [17]. In December 2018, I was invited by the Society for Industrial and Applied Mathematics (SIAM) to write a book with the general solution. This has been the main work of this year. Since this general solution is based on tensorial calculus (Ricci algebra) and many mathematics procedures and tricks borrowed from theoretical physics, the scope of book has gone much more beyond the presentation of the solution. Basically, by writing this book, I've obtained a new theory of observability.

The current theory of nonlinear observability, does not capture/exploit the key features that are intimately related to the concept of observability. This results in two important limitations:

The key to overcome the two above limitations, consists in building a new theory of observability that accounts for the group of invariance that is inherent to the concept of observability. This is the typical manner the research in physics has always proceeded. To this regard, I wish to emphasize that the derivation of the basic equations of any physics theory (e.g., the General Relativity, the Yang Mills theory, the Quantum Chromodynamics) starts precisely from the characterization of the group of invariance of the theory.

One of the major novelties introduced by this book is the characterization of the group of invariance of observability and, regarding the case of unknown inputs, the characterization of a subgroup that was called the Simultaneous Unknown Input Output transformations' group.

In summary, the book provides several novelties with respect to the existing literature in control theory. Specifically, the reader will learn the following:

I believe this book could be an opportunity for control and information theory communities to borrow basic mathematics, tricks, types of reasoning from theoretical physics to revisit many aspects of control and information theory.