A One-Step Visual-Inertial Ego-Motion Estimation using Photometric Feedback

This article presents a robust brightness gradient-based estimation strategy for small aerial robots. The proposed nonlinear observer is capable of estimating the flight altitude and ego-motion by the fusion of monocular vision and inertial measurement unit feedback. The novelty primarily lies in the implementation of the gradient-based featureless approach and the direct use of photometric feedback in the state and output vectors. Under the single-plane assumption, the proposed framework permits the entire estimation process to be accomplished efficiently in a single iterative step without the need for feature detection and tracking or pre-computation of optic flow as commonly seen in conventional methods. The nonlinear and featureless implementation reduces the computational demand, enlarges the region of attraction, and markedly improves the robustness of the ego-motion estimation against scenes with scarce features when compared to Kalman-based estimators and feature-based methods. We conducted extensive flight experiments with different flying patterns and textures to evaluate the performance of the proposed observer. The results reveal that the root-mean-square error in the altitude estimates is approximately 10% for flights at ≈ 40-130 cm above the ground, comparable to feature-based estimators. Nevertheless, the devised observer does better than feature-based methods when deployed on low-textured scenes or with low-resolution blurry images.