Performance Evaluation of Vision-Based Algorithms for MAVs

An important focus of current research in the field of Micro Aerial Vehicles (MAVs) is to increase the safety of their operation in general unstructured environments. Especially indoors, where GPS cannot be used for localization, reliable algorithms for localization and mapping of the environment are necessary in order to keep an MAV airborne safely. In this paper, we compare vision-based real-time capable methods for localization and mapping and point out their strengths and weaknesses. Additionally, we describe algorithms for state estimation, control and navigation, which use the localization and mapping results of our vision-based algorithms as input.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065

Frequency domain subpixel registration using HOG phase correlation

We present a novel frequency-domain image registration technique, which employs histograms of oriented gradients providing subpixel estimates. Our method involves image filtering using dense Histogram of Oriented Gradients (HOG), which provides an advanced representation of the images coping with real-world registration problems such as non-overlapping regions and small deformations. The proposed representation retains the orientation information and the corresponding weights in a multi-dimensional representation. Furthermore, due to the overlapping local contrast normalization characteristic of HOG, the proposed Histogram of Oriented Gradients - Phase Correlation (HOG-PC) method improves significantly the estimated motion parameters in small size blocks. Experiments using sequences with and without ground truth including both global and local/multiple motions demonstrate that the proposed method out- performs the state-of-the-art in frequency-domain motion estimation, in the shape of phase correlation, in terms of subpixel accuracy and motion compensation prediction for a range of test material, block sizes and motion scenarios