Bird\u27s Eye View: Cooperative Exploration by UGV and UAV

This paper proposes a solution to the problem of cooperative exploration using an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). More specifically, the UGV navigates through the free space, and the UAV provides enhanced situational awareness via its higher vantage point. The motivating application is search and rescue in a damaged building. A camera atop the UGV is used to track a fiducial tag on the underside of the UAV, allowing the UAV to maintain a fixed pose relative to the UGV. Furthermore, the UAV uses its front facing camera to provide a birds-eye-view to the remote operator, allowing for observation beyond obstacles that obscure the UGV’s sensors. The proposed approach has been tested using a TurtleBot 2 equipped with a Hokuyo laser ranger finder and a Parrot Bebop 2. Experimental results demonstrate the feasibility of this approach. This work is based on several open source packages and the generated code will be available online

Optical blur disturbs – the influence of optical-blurred images in photogrammtry

Photogrammetric processes such as camera calibration, feature and target detection and referencing are assumed to strongly depend on the quality of the images that are provided for the process. Consequently, motion and optically blurred images are usually excluded from photogrammetric processes to supress their negative influence. To evaluate how much optical blur is acceptable and how large the influence of optical blur is on photogrammetric procedures a variety of test environments were established. These were based upon previous motion blur research and included test fields for the analysis of camera calibration. For the evaluation, a DSLR camera as well as Lytro Illum light field camera were used. The results show that optical blur has a negative influence on photogrammetric procedures, mostly automatic target detection. With the intervention of an experienced operator and the use of semi-automatic tools, acceptable results can be established

Geometric Inference with Microlens Arrays

This dissertation explores an alternative to traditional fiducial markers where geometric information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation of markers in an image. We present markers fabricated from microlenses whose appearance changes depending on the marker\u27s orientation relative to the camera. First, we show how to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality

Vision-Based Control of Unmanned Aerial Vehicles for Automated Structural Monitoring and Geo-Structural Analysis of Civil Infrastructure Systems

The emergence of wireless sensors capable of sensing, embedded computing, and wireless communication has provided an affordable means of monitoring large-scale civil infrastructure systems with ease. To date, the majority of the existing monitoring systems, including those based on wireless sensors, are stationary with measurement nodes installed without an intention for relocation later. Many monitoring applications involving structural and geotechnical systems require a high density of sensors to provide sufficient spatial resolution to their assessment of system performance. While wireless sensors have made high density monitoring systems possible, an alternative approach would be to empower the mobility of the sensors themselves to transform wireless sensor networks (WSNs) into mobile sensor networks (MSNs). In doing so, many benefits would be derived including reducing the total number of sensors needed while introducing the ability to learn from the data obtained to improve the location of sensors installed. One approach to achieving MSNs is to integrate the use of unmanned aerial vehicles (UAVs) into the monitoring application. UAV-based MSNs have the potential to transform current monitoring practices by improving the speed and quality of data collected while reducing overall system costs. The efforts of this study have been chiefly focused upon using autonomous UAVs to deploy, operate, and reconfigure MSNs in a fully autonomous manner for field monitoring of civil infrastructure systems. This study aims to overcome two main challenges pertaining to UAV-enabled wireless monitoring: the need for high-precision localization methods for outdoor UAV navigation and facilitating modes of direct interaction between UAVs and their built or natural environments. A vision-aided UAV positioning algorithm is first introduced to augment traditional inertial sensing techniques to enhance the ability of UAVs to accurately localize themselves in a civil infrastructure system for placement of wireless sensors. Multi-resolution fiducial markers indicating sensor placement locations are applied to the surface of a structure, serving as navigation guides and precision landing targets for a UAV carrying a wireless sensor. Visual-inertial fusion is implemented via a discrete-time Kalman filter to further increase the robustness of the relative position estimation algorithm resulting in localization accuracies of 10 cm or smaller. The precision landing of UAVs that allows the MSN topology change is validated on a simple beam with the UAV-based MSN collecting ambient response data for extraction of global mode shapes of the structure. The work also explores the integration of a magnetic gripper with a UAV to drop defined weights from an elevation to provide a high energy seismic source for MSNs engaged in seismic monitoring applications. Leveraging tailored visual detection and precise position control techniques for UAVs, the work illustrates the ability of UAVs to—in a repeated and autonomous fashion—deploy wireless geophones and to introduce an impulsive seismic source for in situ shear wave velocity profiling using the spectral analysis of surface waves (SASW) method. The dispersion curve of the shear wave profile of the geotechnical system is shown nearly equal between the autonomous UAV-based MSN architecture and that taken by a traditional wired and manually operated SASW data collection system. The developments and proof-of-concept systems advanced in this study will extend the body of knowledge of robot-deployed MSN with the hope of extending the capabilities of monitoring systems while eradicating the need for human interventions in their design and use.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169980/1/zhh_1.pd

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application