The VIMAD project has two main goals: - (technological) build a robust and reliable perception system, only based on visual and inertial measurements, to enhance the navigation capabilities of fully autonomous micro aerial drones; - (scientific) acquire a deep comprehension of the problem of fusing visual and inertial measurements (from now on the Visual-Inertial structure from motion, VISfM). The perception system will be embedded on micro drones to make them able to safely and autonomously navigate in GPS denied and unknown environments and even to perform aggressive manoeuvres. In particular, with unknown environments, we mean environments that are not equipped with motion capture systems or any external sensor. Perception is still the main problem for high-performance robotics. Once the perception problem is assumed solved, for example by the use of external motion-capture systems, then established control techniques allow for highly performing systems [19,28]. A perception system suitable for a micro aerial vehicle must satisfy sever constraints, due to the small size and, consequently, the low allowed payload. This imposes the employment of low weight sensors and low computational complexity algorithms. In this context inertial sensors and monocular cameras, thanks to their complementary characteristics, low weight, low cost and widespread use, represent an interesting sensor suite. On the other hand, current technologies for navigation only based on visual and inertial sensors have the following strong limitations: - The localization task is achieved via recursive algorithms which need initialization. This means that they are not fully autonomous and, more importantly, they are not robust against any unmodeled event (e.g. system failure) which requires the algorithm to be re-initialized; - They are not enough precise in order to allow a micro aerial vehicle to undertake aggressive manoeuvres and, more in general, to accomplish sophisticated tasks. To overcome these limitations our perception system will be developed by relying on the following three new paradigms: - Use of the closed-form solution to the visual-inertial structure from motion problem introduced in [23,24]; - Exploitation of the information contained in the dynamics of the drones; - Use of the observability tool developed in [22] The first paradigm will allow the perception system to be able to initialize (or reinitialize) the localization task, without external support. In other words, it will make the localization task fully autonomous and robust against any unmodeled event like a kidnapping. Additionally, it can be used to introduce a low-cost data association method. The second paradigm will enhance the perception capabilities in terms of precision. This is important in order to accomplish aggressive manoeuvres. Finally, the third paradigm will allow us both to acquire a deeper comprehension of the VISfM and hopefully to design new and more effective sensor arrangements. This scientific topic deserves in our opinion a deep theoretical investigation since the perception system of most mammals relies precisely on visual and vestibular signals. A deep scientific comprehension of this problem could allow the robotics community to introduce new technologies for navigation. Specifically, we will approach this fundamental problem by proceeding in two main steps. In the former we will investigate an open problem in the framework of control theory, which is the Unknown Input Observability (UIO), namely the observability analysis in the case of unknown inputs; the latter is the use of the results obtained for UIO to investigate the observability properties of the VISfM in the case of missing inertial inputs and eventually to design new sensor arrangements.