Systems with multiple sensors can provide information unavailable from a single source, and complementary sensory characteristics can improve accuracy and robustness to many vulnerabilities as well. Explicit pose measurements are often performed either with high frequency or precision, however visuo-inertial sensors present both features. Vision algorithms accurately measure pose at low frequencies, but limit the drift due to integration of inertial data. Inertial measurement units yield incremental displacements at high frequencies that initialize vision algorithms and compensate for momentary loss of sight. This thesis analyzes two aspects of that problem. First, we survey direct visual tracking methods for pose estimation, and propose a new technique based on the normalized crosscorrelation, region and pixel-wise weighting together with a Newton-like optimization. This method can accurately estimate pose under severe illumination changes. Secondly, we investigate the data fusion problem from a control point of view. Main results consist in novel observers for concurrent estimation of pose, IMU bias and self-calibration. We analyze the rotational dynamics using tools from nonlinear control, and provide stable observers on the group of rotation matrices. Additionally, we analyze the translational dynamics using tools from linear time-varying systems, and propose sufficient conditions for uniform observability. The observability analyses allow us to prove uniform stability of the observers proposed. The proposed visual method and nonlinear observers are tested and compared to classical methods using several simulations and experiments with real visuo-inertial data.
