Localization and detection of wireless embeddable structural sensors using an unmanned aerial vehicle in the absence of visual markers

The objective of this thesis is to develop a fully integrated UAV based platform for autonomous collection of data from embedded sensors. Passive (battery-less) embedded sensors provide means for periodic long-term monitoring of civil structures like bridges. However, collection of data from these sensors requires extensive manual effort of locating them. UAVs can automate this process, although localization of these embedded tags in absence of visual markers pose a challenge. A RF (13.56MHz) reader is used to capture data from RF tags wirelessly. Different tag coil sizes are tested to observe effects on read range as well as to characterize the interaction volume between reader and tag. The UAV platform is integrated with the RF reader to autonomously capture data from tags using GPS based localization. Different sensor configurations are tested and characterized to meet the requirements of X,Y,Z localization set by the reader and tag interaction volume. Flight characteristics are also observed for various UAV navigation parameters. Results suggest that by using low-cost RTK GPS unit, the UAV is capable of detecting and localizing RF tags without any visual markers or aides.Electrical and Computer Engineerin

Probabilistic Surfel Fusion for Dense LiDAR Mapping

With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. However, none of the previous approaches were able to effectively exploit this redundancy in a dense LiDAR mapping problem. In this paper, we present a new approach for dense LiDAR mapping using probabilistic surfel fusion. The proposed system is capable of reconstructing a high-quality dense surface element (surfel) map from spatially redundant multiple views. This is achieved by a proposed probabilistic surfel fusion along with a geometry considered data association. The proposed surfel data association method considers surface resolution as well as high measurement uncertainty along its beam direction which enables the mapping system to be able to control surface resolution without introducing spatial digitization. The proposed fusion method successfully suppresses the map noise level by considering measurement noise caused by laser beam incident angle and depth distance in a Bayesian filtering framework. Experimental results with simulated and real data for the dense surfel mapping prove the ability of the proposed method to accurately find the canonical form of the environment without further post-processing.Comment: Accepted in Multiview Relationships in 3D Data 2017 (IEEE International Conference on Computer Vision Workshops

Low-cost, stand-off, 2D+3D face imaging for biometric identification using Fourier transform profilometry

EDU meets the goals for the 2D+3D face imager of Class 1M eye-safe operation, 2D+3D face capture at \u3e20-m stand-off distance, ~1-mm lateral resolution, ~1-mm rang

Low-cost,stand-off, 2D+3D face imaging for biometric identification using Fourier transform profilometry –Update

Lockheed Martin Coherent Technologies is developing laser-based technologies for stand-off 2D+3D face imaging for biometric identification. Among other potential industrial, commercial, and governmental users, the Department of Homeland Security (DHS) and the Department of Defense (DoD) desire the ability to capture biometric data from minimally cooperative subjects with a minimally invasive system at stand-off distances. The initial applications are fixed installations for relatively large volume access points such as security check points and transportation gateways for which minimal cooperation, stand-off operation, and real-time operation are desired so that the biometric identification process will have little impact on traffic flow. Last year we presented a paper on the development and testing of a 2D+3D face imager breadboard based on th

FPGA-Based Real-Time SLAM

This project created a proof of concept SLAM sensor suite capable of remotely observing and mapping areas by combining real-time stereo camera imagery with distance measurements and localization data to generate a 3D depth map and 2D floorplan of its environment. The system used a Xilinx Zynq SoC containing an embedded ARM processor and FPGA fabric, and implemented unique SLAM processing functionality using both embedded software and parallelized custom logic

External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Intelligent Controls for Net-Zero Energy Buildings

The goal of this project is to develop and demonstrate enabling technologies that can empower homeowners to convert their homes into net-zero energy buildings in a cost-effective manner. The project objectives and expected outcomes are as follows: • To develop rapid and scalable building information collection and modeling technologies that can obtain and process “as-built” building information in an automated or semiautomated manner. • To identify low-cost measurements and develop low-cost virtual sensors that can monitor building operations in a plug-n-play and low-cost manner. • To integrate and demonstrate low-cost building information modeling (BIM) technologies. • To develop decision support tools which can empower building owners to perform energy auditing and retrofit analysis. • To develop and demonstrate low-cost automated diagnostics and optimal control technologies which can improve building energy efficiency in a continual manner

FUSION OF 3D POINT CLOUDS WITH TIR IMAGES FOR INDOOR SCENE RECONSTRUCTION

Obtaining accurate 3D descriptions in the thermal infrared (TIR) is a quite challenging task due to the low geometric resolutions of TIR cameras and the low number of strong features in TIR images. Combining the radiometric information of the thermal infrared with 3D data from another sensor is able to overcome most of the limitations in the 3D geometric accuracy. In case of dynamic scenes with moving objects or a moving sensor system, a combination with RGB cameras and profile laserscanners is suitable. As a laserscanner is an active sensor in the visible red or near infrared (NIR) and the thermal infrared camera captures the radiation emitted by the objects in the observed scene, the combination of these two sensors for close range applications are independent from external illumination or textures in the scene. This contribution focusses on the fusion of point clouds from terrestrial laserscanners and RGB cameras with images from thermal infrared mounted together on a robot for indoor 3D reconstruction. The system is geometrical calibrated including the lever arm between the different sensors. As the field of view is different for the sensors, the different sensors record the same scene points not exactly at the same time. Thus, the 3D scene points of the laserscanner and the photogrammetric point cloud from the RGB camera have to be synchronized before point cloud fusion and adding the thermal channel to the 3D points

Landslide mapping and characterization through Infrared Thermography (IRT): Suggestions for a methodological approach from some case studies

In this paper, the potential of Infrared Thermography (IRT) as a novel operational tool for landslide surveying, mapping and characterization was tested and demonstrated in different case studies, by analyzing various types of instability processes (rock slide/fall, roto-translational slide-flow). In particular, IRT was applied, both from terrestrial and airborne platforms, in an integrated methodology with other geomatcs methods, such as terrestrial laser scanning (TLS) and global positioning systems (GPS), for the detection and mapping of landslides’ potentially hazardous structural and morphological features (structural discontinuities and open fractures, scarps, seepage and moisture zones, landslide drainage network and ponds). Depending on the study areas’ hazard context, the collected remotely sensed data were validated through field inspections, with the purpose of studying and verifying the causes of mass movements. The challenge of this work is to go beyond the current state of the art of IRT in landslide studies, with the aim of improving and extending the investigative capacity of the analyzed technique, in the framework of a growing demand for effective Civil Protection procedures in landslide geo-hydrological disaster managing activities. The proposed methodology proved to be an effective tool for landslide analysis, especially in the field of emergency management, when it is often necessary to gather all the required information in dangerous environments as fast as possible, to be used for the planning of mitigation measures and the evaluation of hazardous scenarios. Advantages and limitations of the proposed method in the field of the explored applications were evaluated, as well as general operative recommendations and future perspectives