SLAM and SfM algorithms typically involve minimization of a cost-function by non-linear least-squares methods. The matrices involved are typically very poorly conditioned, making the procedure sensitive to numerical precision effects. Ensuring accuracy therefore entails the use of high-precision floating-point data-types for representation and compute. In this paper, a square-root filtering approach to EKF-based SfM is presented and is shown to be capable of operating with lower-precision arithmetic than the EKF, while sacrificing only a little in accuracy...
Authors
Yeongseon Lee
Omesh Tickoo
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