Determination of a vehicle or person's position and/or orientation is a critical task for a multitude of applications ranging from automated cars and first responders to missiles and fighter jets. Most of these applications rely primarily on global navigation satellite systems, e.g., GPS, which are highly vulnerable to degradation whether by environmental factors or malicious actions. The use of inertial navigation techniques has been shown to provide increased reliability of navigation systems in these situations. Due to advances in MEMS technology and processing capabilities, the use of small and low-cost inertial measurement units (IMUs) are becoming increasingly feasible, which results in small size, weight and power (SWaP) solutions. A known limitation of MEMS IMUs are errors that causes the navigation solution to drift; furthermore, calibration and initialization are challenging tasks. In this paper, we investigate the use of multiple IMUs to aid in calibrating the navigation system and obtaining accurate initialization by performing fine alignment. By using a centralized filter, physical constraints between the multiple IMUs on a rigid body are leveraged to provide relative updates, which in turn aids in the estimation of the individual biases and scale-factors. Developed algorithms will be validated through simulation and actual measurements using low-cost IMUs.