Alternative Position Estimation Systems for Micro Air Vehicles

Micro air vehicles (MAVs) is a technology that is becoming more and more important and popular nowadays. It is used as a tool to deal with different tasks that were not possible in the past. For most MAV models, the GPS sensor is the only way of estimating its pose in the environment. However, besides the fact of not having a secondary position estimation system besides the GPS, this is also risky because the GPS may fail like any other sensor. To overcome this weakness and make the MAVs more robust to autonomous tasks, the research community proposed many different localization systems for different constraints. In this chapter, the most popular, recent, and important MAV localization systems are reviewed, as well as the promising future works in this field

Season-invariant GNSS-denied visual localization for UAVs

Localization without Global Navigation Satellite Systems (GNSS) is a critical functionality in autonomous operations of unmanned aerial vehicles (UAVs). Vision-based localization on a known map can be an effective solution, but it is burdened by two main problems: places have different appearance depending on weather and season, and the perspective discrepancy between the UAV camera image and the map make matching hard. In this work, we propose a localization solution relying on matching of UAV camera images to georeferenced orthophotos with a trained convolutional neural network model that is invariant to significant seasonal appearance difference (winter-summer) between the camera image and map. We compare the convergence speed and localization accuracy of our solution to six reference methods. The results show major improvements with respect to reference methods, especially under high seasonal variation. We finally demonstrate the ability of the method to successfully localize a real UAV, showing that the proposed method is robust to perspective changes.Comment: Published in IEEE Robotics and Automation Letters (Volume: 7, Issue: 4, October 2022

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Training neural networks for informal road extraction

Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria 2022.Roads found in informal settlements arise out of convenience are often not recorded or maintained by authorities. This may cause issues with service delivery, sustainable development and crisis mitigation, including COVID-19. Therefore, the aim of extracting informal roads from remote sensing images is of importance. Existing techniques aimed at the extraction of formal roads are not completely suitable for the problem due to the complex physical and spectral properties that informal roads pose. The only existing approaches for informal roads, namely [62, 82], do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities due to the way they learn through training. They require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data sets. A problem that has been shown to come up when working with computer vision data sets is data set bias. Data set bias adds to the already existing problem of machine learning algorithms called overfitting. This paper implements a neural network developed for formal roads to extract informal roads from three data sets digitised by this research group to investigate the presence of data set bias. Three different geological areas from South Africa are digitised. We implement the GAN-UNet model that obtained the highest F1-score in a 2020 review paper [1] on the state-of-the-art deep learning models used to extract formal roads. We present quantitative and qualitative results that concludes the presence of data set bias. We then present further work that can be done to create a robust training data set for the development of an automatic informal road extraction model.- Data Science Africa 2021 Project (PI: Inger Fabris-Rotelli) - Centre for Artificial Intelligence Research - CoE-MaSS grant (2022 grant: ref #2022-018-MAC-Road)StatisticsMSc (Advanced Data Analytics)Unrestricte