A survey on vision-based UAV navigation

Abstract

Research on unmanned aerial vehicles (UAV) has been increasingly popular in the past decades, and UAVs have been widely used in industrial inspection, remote sensing for mapping & surveying, rescuing, and so on. Nevertheless, the limited autonomous navigation capability severely hampers the application of UAVs in complex environments, such as GPS-denied areas. Previously, researchers mainly focused on the use of laser or radar sensors for UAV navigation. With the rapid development of computer vision, vision-based methods, which utilize cheaper and more flexible visual sensors, have shown great advantages in the field of UAV navigation. The purpose of this article is to present a comprehensive literature review of the vision-based methods for UAV navigation. Specifically on visual localization and mapping, obstacle avoidance and path planning, which compose the essential parts of visual navigation. Furthermore, throughout this article, we will have an insight into the prospect of the UAV navigation and the challenges to be faced.     

Super-resolution enhancement of UAV images based on fractional calculus and POCS

Abstract

A super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets (POCS) for unmanned aerial vehicles (UAVs) images. The representative problems of UAV images including motion blur, fisheye effect distortion, overexposed, and so on can be improved by the proposed algorithm. The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS. The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform (SIFT) for matching. The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail. The objective indices and subjective effect are compared between the proposed and other methods. The experimental results indicate that the proposed method outperforms other algorithms in most cases, especially in the structure and detail clarity of the reconstructed images.     

A study of a matching pixel by pixel (MPP) algorithm to establish an empirical model of water quality mapping,as based on unmanned aerial vehicle (UAV) images

Linear regression models are a popular choice for the relationships between water quality parameters and bands (or band ratios) of remote sensing data. However, this research regards the phenomena of mixed pixels, specular reflection, and water fluidity as the challenges to establish a robust regression model. Based on the data of measurements in situ and remote sensing data, this study presents an enumeration-based algorithm, called matching pixel by pixel (MPP), and tests its performance in an empirical model of water quality mapping. Four small reservoirs, which cover a mere several hundred-thousand m², in Kinmen, Taiwan, are selected as the study sites. The multispectral sensors, carried on an unmanned aerial vehicle (UAV), are adopted to acquire remote sensing data regarding water quality parameters, including chlorophyll-a (Chl-a), Secchi disk depth (SDD), and turbidity in the reservoirs. The experimental results indicate that, while MPP can reduce the influence of specular reflection on regression model establishment, specular reflection does hamper the correction of thematic map production. Due to water fluidity, sampling in situ should be followed by UAV imaging as soon as possible. Excluding turbidity, the obtained estimation accuracy can satisfy the national standard.