SLAM and SfM algorithms involve minimization of a cost-function by non-linear least-squares methods. The matrices involved are typically poorly conditioned, making the procedure sensitive to numerical precision effects. Ensuring accuracy therefore entails the use of high-precision floating-point arithmetic. In this work, a factorization approach to EKF-based SfM is presented and is shown to be capable of operating with integer arithmetic-the first such implementation to the best of our knowledge. This is important given the increasing need to implement advanced vision-based capabilities on low-power embedded and mobile processors. An evaluation of the computational complexity shows that the proposed approach typically requires fewer computations than the EKF in practice, resulting in an algorithm that is both numerically more robust and computationally less intensive.