Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

FoundLoc: Vision-based Onboard Aerial Localization in the Wild

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms

A Systematic Literature Survey of Unmanned Aerial Vehicle Based Structural Health Monitoring

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs based on their aerodynamics, payload, design of build, and its applications. Further, the thesis presents the payload product line to facilitate the SHM tasks, details the different applications of UAVs exploited in the last decade to support civil structures, and discusses the critical challenges faced in UASHM applications across various domains. Finally, the thesis presents two artificial neural network-based structural damage detection models and conducts a detailed performance evaluation on multiple platforms like edge computing and cloud computing

Real-Time Implementation of Vision-Aided Monocular Navigation for Small Fixed-Wing Unmanned Aerial Systems

The goal of this project was to develop and implement algorithms to demonstrate real-time positioning of a UAV using a monocular camera combined with previously collected orthorectified imagery. Unlike previous tests, this project did not utilize a full inertial navigation system (INS) for attitude, but instead had to rely on the attitude obtained by inexpensive commercial off-the-shelf (COTS) autopilots. The system consisted of primarily COTS components and open-source software, and was own over Camp Atterbury, IN for a sequence of flight tests in Fall 2015. The system obtained valid solutions over much of the flight path, identifying features in the flight image, matching those features with a database of features, and then solving both the 6DOF solution, and an attitude-aided 3DOF solution. The tests demonstrated that such attitude aiding is beneficial, since the horizontal DRMS of the 6DOF solution was 59m, whereas the 3DOF solution DRMS was 15m. Post processing was done to improve the algorithm to correct for system errors, obtaining a 3DOF solution DRMS of 8.22 meters. Overall, this project increased our understanding of the capabilities and limitations of real-time vision-aided navigation, and demonstrated that such navigation is possible on a relatively small platform with limited computational power

Self-Describing Fiducials for GPS-Denied Navigation of Unmanned Aerial Vehicles

Accurate estimation of an Unmanned Aerial Vehicle’s (UAV’s) location is critical for the operation of the UAV when it is controlled completely by its onboard processor. This can be particularly challenging in environments in which GPS is not available (GPS-denied). Many of the options previously explored for estimation of a UAV’s location without the use of GPS require more sophisticated processors than can feasibly be mounted on a UAV because of weight, size, and power restrictions. Many options are also aimed at indoor operation without the range capabilities to scale to outdoor operations. This research explores an alternative method of GPS-denied navigation which utilizes line-of-sight measurements to self-describing fiducials to aid in position determination. Each self-describing fiducial is an easily identifiable object fixed at a specific location. Each fiducial relays data containing its specific location to the observing UAV. The UAV can measure its relative position to the fiducial using camera images. This measurement can be combined with measurements from an Inertial Measurement Unit (IMU) to obtain a more accurate estimate of the UAV’s location. In this research, a simulation is used to validate and assess the performance of algorithms used to estimate the UAV’s position using these measurements. This research analyzes the effectiveness of the estimation algorithm when used with various IMUs and fiducial spacings. The effect of how quickly camera images of fiducials can be captured and processed is also analyzed. Preparations for demonstrating this system with hardware are then presented and discussed, including options for fiducial type and a way to measure the true position of the UAV. The results from the simulated scenarios and the hardware demonstration preparation are analyzed, and future work is discussed