Efficient Pose Prediction with Rational Regression applied to vSLAM

Compared to polynomial splines, rational func- tions are known to be more efficient and well-behaved data fitting models. However, due to the potential presence of zeros in their denominator, rational functions tend to yield notoriously hard optimization problems. In this work, we present a novel least squares method for 6D pose prediction that employs rational regression. Our method can accommodate fixed data points and is able to circumvent the occurrence of zeros for rational quadratic interpolants. We demonstrate the suitability of rational quadratics for pose prediction by applying our approach to real data from the feature tracking stage of a real- time visual SLAM system and showing that it yields far more stable predictions when compared to state-of-the-art rational and polynomial spline methods.