Robust estimation of camera-tilt for iFMI based underwater photo-mapping using a calibrated monocular camera

Abstract

An autonomous aerial or underwater robot can create photo-maps using a downward looking camera to not only compute its odometry visually but also to provide a useful and intuitively understandable representation of the environment explored by it. The improved Fourier Mellin Invariant (iFMI) registration is a spectral registration method, which has specific benefits, especially high robustness in featureless scenarios, but it only allows registrations of 2D translations, rotation, and scale. The method is extended here to incorporate tilt using parallax information. To this end, we extend the well-known four-point algorithm for planar homography. We show that using the decomposition of the planar homography to compute the tilt is very noise-prone, and propose a way of increasing this accuracy based on a parallax to noise metric. Although our general approach can be used with local scale invariant image features, we implement the tilt-correction based on an extension of our frequency-based approach to determine the image motion-field. Two experiments are presented to show the efficacy and applicability of our approach: An analysis of a simulated data set with ground truth is used to quantify the robustness of our novel method relative to the same four-point method using only SIFT (Scale Invariant Feature Transform) features. A second data set is used to present similar results with real-world data.