The use of a Bayesian filter (e.g., Kalman filter) for the fusion of information from satellite positioning and inertial navigation is a common approach in many applications, where the knowledge of position, velocity, and attitude in space are of great interest. The correctness of these estimates depends on many factors, among others the quality of the sensor measurements and the errors within, which are directly reflected in the filter design. A calibration process allows compensating for deterministic influences (which in return improve for instance qualitatively the attitude initialization) and their inherent stochastic error signals required for filtering. This thesis presents in the first part the development of methods to perform a thorough calibration of different sensors in-lab under controlled conditions and in-field for a simplified calibration with limited resources and equipment. The stochastic properties of error signals are analyzed in the second part. A novel approach called Generalized Method of Wavelet Moments (GMWM) allows investigating the error structure using wavelets, which is similar to the Allan variance. An intuitive online tool is presented, which grants simplified access to the GMWM framework that provides a consistent, identifiable, and computationally efficient estimation of stochastic model parameters. The parameters of these error models are then made dependent on an external covariate such as temperature or motion. Indeed, it is experimentally confirmed that these properties shape the stochastic behavior of the measurements and how the stochastic parameters relate functionally to the influence of the covariate. Later, such knowledge is included in the filter for the correct estimation of confidence levels. The successful implementation of these proposed concepts is validated in a fully functional drone-system for mapping purposes. A real-time calibration scheme is applied first in-lab, later in-field to initialize the navigation processor. Apart from the benefit of achieving considerably better estimates of the attitude, and in case of satellite signal outage also of the position, the calibration allows for a simplified fusion of redundant inertial sensors. The improved performances through calibration and sensor redundancy are attractive to drone mapping applications relying on an accurate direct or integrated orientation such as lightweight airborne laser scanning systems or frame-cameras, which are utilized in the experiments.