PRISM :: Browsing by Author "Lichti, Derek D."

Browsing by Author "Lichti, Derek D."

Now showing 1 - 19 of 19

Open Access
Advancing Measurement and Modelling of Glacier Change Using Unmanned Aerial Vehicles and Structure-From-Motion
(2019-09-09) Bash, Eleanor A.; Moorman, Brian J.; McDermid, Gregory J.; Marshall, Shawn; Lichti, Derek D.; Mueller, Derek R.
Glaciers throughout Canada are responding to climate change with rapid changes in mass balance. There are limitations in current methods of measuring and predicting these changes in mass balance, including accessibility, spatial and temporal resolution of remotely sensed data, and cost of data acquisition. Technological developments in unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) have created new opportunities to overcome these limitations. This dissertation investigated uncertainties in UAV-SfM data and used that data to understand spatial patterns and drivers of summer glacier melt. A study of glacier snow surface reconstruction in the Canadian Rockies used lidar data acquired simultaneously with UAV imagery to assess the spatial distribution of errors in the UAV-SfM data. The study revealed patterns in the errors related to snow surface illumination which must be considered when using UAVs over snow covered glaciers. Short term summer melt in the ablation zone of a glacier in the Canadian Arctic was investigated using UAV surveys. The study showed that UAV-SfM melt measurements agreed with ablation stake measurements and was a reliable method of measuring distributed melt patterns. The study found the lower limits on change detection were related to flying height and dGPS precision. A melt model was used to estimate surface melt for the three-day window where UAV-SfM measurements were collected and model results were validated against spatially distributed measurements. This study revealed patterns in model error which show that simplified melt models fail to capture important melt drivers on the glacier surface. The model errors would have cumulative effects in long term projections, which would lead to significant misrepresentation of total surface melt. UAV and SfM technologies were shown to be an effective method for gathering highly detailed information on glacier surface characteristics and change. However, the technology is not the answer to every problem and limitations still exist in its appropriate application. This work shows the utility of the data in advancing modelling efforts where site visits are not feasible. The dissertation ultimately demonstrates that studies can be strengthened using UAV-SfM data as one tool of many to address questions of glacier change.
Open Access
Automated Floor Plan and Building Model Creation for Cultural Heritage Buildings from Laser Scanner Data
(2021-06-21) Pexman, Katherine; Lichti, Derek D.; Dawson, Peter; O'Keefe, Kyle; Detchev, Ivan
This research project developed and implemented an automated modelling system to create 2D floor plans and 3D building models of heritage sites. Without building plans, it is more difficult for an historic building to receive historic designation and restoration funding. Under current practice, the creation of such physical documentation is expensive and time-consuming. Physical documentation can include as-built architectural plans, elevations, profiles and photographs, whereas historic documentation includes important documents, artefacts, and historic photographs or archives. Important heritage sites whose building plans have been lost or destroyed or become inaccurate through renovations are often left abandoned or not kept up properly because they are unable to receive the necessary support. The current modelling process involves the utilization of CAD software and a trained modeller to digitally draw a 2D floor plan, or a more complex 3D building model, overlain upon the point cloud data collected by a laser scanner. As currently applied, point cloud modelling requires inefficient manual manipulation, editing and rendering of large datasets within a CAD environment to produce floor plans and building models. This research project used statistical methods such as principal components analysis (PCA) and M-estimator sample consensus (MSAC) to automatically detect building features from a point cloud captured through a 3D terrestrial laser scan (TLS) of the building site. Two novel methods were developed in this work to help in the automation of floor plan and building model creation. The first was a novel methodology for the automated separation of storeys within a multi-level, multi-storey building. The second was a novel methodology for the automated detection of doors and windows within a point cloud using a wall-defined search space. These new methods were implemented as components in an end-to-end modelling strategy for the creation of floor plans and building models, the final output of which is written in a CAD-accessible file format. The modelling strategy showed an overall accuracy of 92.75% for the tested datasets, demonstrating the ability of the developed program to accurately produce both 2D floor plans and 3D building models of multi-level building storeys with door and window features. The development of this automated process will allow a non-geomatics expert to create floor plans and/or building models of sites with significantly less manual effort and reduced cost. This will increase the ability of heritage sites to receive historic designation, allowing them to be better preserved over time.
Open Access
Automatic Registration of Imagery to Mobile LiDAR Maps
(2024-02-15) Jones, Kent Douglas; Lichti, Derek D.; Detchev, Ivan Denislavov; Yang, Hongzhou; El-Sheimy, Naser M.; Chapman, Michael Alastair
Mapping in 3D that records geospatial data from platform-mounted sensors with digital twinning supports maintenance and future planning of civil infrastructure. Three-dimensional mapping is efficiently performed with a Mobile Mapping System (MMS). This research demonstrates camera-only registration of subsequently captured images to an MMS point-cloud for updating MMS datasets. The research resolves key issues with inherent resolution differences between MMS laser scanner point-clouds and camera images by bridging differences between MMS point-clouds and camera images using a synthetic camera image (SCI). SCI are used to determine the approximate pose or coarse register the camera image to the MMS point cloud. The SCI coarse registration precision is maximized by generating surfaces, interpolating intensity values, and reducing noise with a median filter. The SCI is processed with a median filter to remove salt-and-pepper noise from the generation methods while preserving edges. Edgeboxes are adapted to find similar features in both SCIs and camera images. These features are then passed through layers of a convolutional neural network to provide a feature descriptor for coarse registration. Real camera images (RCI) are processed to mitigate resolution differences with the SCIs. The RCI is downsampled to align with the spatial resolution of the SCIs. Robust features are used to register the RCI to the SCIs. SIFT is used for fine registration between RCIs and SCIs generated from dense point-clouds. Landmark features are used for registration of RCIs to SCIs generated from MMS point-clouds. The edgebox parameters require tuning to detect the same features in two disparate image sets. The fourth layer of AlexNet was found to provide the most ideal feature descriptor for registration between RCIs and SCIs. The approximate location of the RCI using SCIs as interpreters between RCI and MMS point-cloud detect scenes at a precision of 97% when changes are less than 20%, and foliage does not exceed 20% of the camera image. This novel application of landmark features aligns with camera-to-camera place recognition precision. The focal length and IOPs do not influence the precision of the registration because the registration precision does not change when different cameras capture the real images.
Open Access
Concrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds
(2020-05-05) Hadavandsiri, Zahra; Lichti, Derek D.; Shahbazi, Mozhdeh M.; Dawson, Peter C.
Concrete structures endure damage and deterioration when subjected to human activities and natural hazards. Early detection of damage on concrete structures is vital to counter the side effects deriving from damage and to allow timely maintenance procedures. This thesis presents a novel approach for high-precision detection of damage on concrete surfaces using terrestrial laser scanner point clouds (PCs). At first, an unsupervised approach is developed that utilizes a robust version of principal component analysis (PCA) classification in order to distinguish between structural damage and outliers present in the data. Numerical simulations are conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of surface damage on the investigated region. The developed method examined on two real datasets, demonstrate the validity of the proposed systematic framework for reliable detection of damage of any type which makes roughness as small as 1 cm or larger on the surface of concrete structures captured with any laser-scanning PC with a minimum spatial resolution of 5 mm point spacing. At second, a supervised approach is developed that employs the outcome of the primary unsupervised classifier in order to accurately annotate the training data without the need for manual labeling. One flume of an aqueduct dataset was used for training the system. This machine learning-based model relies on a support vector machine (SVM) algorithm to train a point-wise defect classifier for locating the concrete damage. This yields an average classification precision and F1-score of 97.33% showing the potential of using machine learning for concrete damage detection. The performance of the prediction model was evaluated on three real datasets. The prediction model can successfully mirror the high performance of the unsupervised method used in the training process. In addition, by exploiting a more extensive variety of geometric features and skipping the intensive computation of the robust PCA, it outperforms the unsupervised classifier in terms of model performance and computational efficiency, respectively. Consequently, the properly trained machine learning system provides reliable diagnosis of the health conditions of large concrete structures that are not computationally feasible to be inspected by the primary unsupervised classifier.
Open Access
Constrained finite element method self-calibration
(1996) Lichti, Derek D.; Chapman, Michael A.
Open Access
Explanation for the seam line discontinuity in terrestrial laser scanner point clouds
(Elsevier, 2019-08) Lichti, Derek D.; Glennie, Craig L.; Al-Durgham, Kaleel; Jahraus, Adam; Steward, Jeremy
The so‐called seam line discontinuity is a phenomenon that can be observed in point clouds captured with some panoramic terrestrial laser scanners. It is an angular discontinuity that is most apparent where the lower limit of the instrument’s angular field‐of‐view intersects the ground. It appears as step discontinuities at the start (0° horizontal direction) and end (180°) of scanning. To the authors’ best knowledge, its cause and its impact, if any, on point cloud accuracy have not yet been reported. This paper presents the results of a rigorous investigation into several hypothesized causes of this phenomenon: differences between the lower and upper elevation angle scanning limits; the presence of a vertical circle index error; and changes in levelling during scanning. New models for the angular observations have been developed and simulations were performed to independently study the impact of each hypothesized cause and to guide the analyses of real datasets. In order to scrutinize each of the hypothesized causes, experiments were conducted with seven real datasets captured with six different instruments: one hybrid‐architecture scanner and five panoramic scanners, one of which was also operated as a hybrid instrument. This study concludes that the difference between the elevation angle scanning limits is the source of the seam line discontinuity phenomenon. Accuracy assessment experiments over real data captured in an indoor test facility demonstrate that the seam line discontinuity has no metric impact on the point clouds.
Open Access
Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images
(2018-04-24) Sun, Weiwei; Wang, Ruisheng; Wang, Xin; Lichti, Derek D.
The semantic segmentation of very high resolution (VHR) remotely sensed images is to assign a categorical label for each pixel, which is an important but unsolved problem in remote sensing. In recent years, fully convolutional networks (FCN) have become the state-of-the-art framework for the semantic segmentation in computer vision. Thus, this work aims to improve the semantic segmentation of VHR images by utilizing FCN. Firstly, we propose a promising framework which achieves the top result (90.6%) on the ISPRS Vaihingen benchmark. In the framework, the proposed FCN-based network obtains a competitive result (90.1%). In addition, we develop the DSM backend to enhance the result of FCN by incorporating complementary information from color images and digital surface model (DSM). Secondly, we propose the recurrent FCN for modeling the continuous context inherent in VHR images. Experimental results demonstrate that the recurrent FCN significantly boosts the performance of FCN by incorporating the local contextual information from patches and the global contextual information between patches.
Open Access
Geometric modelling and calibration of a spherical camera imaging system
(2020-04-16) Lichti, Derek D.; Jarron, David; Tredoux, Wynand; Shahbazi, Mozhdeh M.; Radovanovic, Robert
The Ladybug5 is an integrated, multi-camera system that features a near-spherical field of view. It is commonly deployed on mobile mapping systems to collect imagery for 3D reality capture. This paper describes an approach for the geometric modelling and self-calibration of this system. The collinearity equations of the pinhole camera model are augmented with five radial lens distortion terms to correct the severe barrel distortion. Weighted relative orientation stability constraints are added to the self-calibrating bundle adjustment solution to enforce the angular and positional stability between the Ladybug5’s six cameras. Results are presented from two calibration data-sets and an independent data-set for accuracy assessment. It is demonstrated that centimetre-level 3D reconstruction accuracy can be achieved with the proposed approach. Moreover, the effectiveness of the lens distortion modelling is demonstrated. Image-space precision and object-space accuracy are improved by 92% and 93%, respectively, relative to a two-term model. The high correlations between lens distortion coefficients were not found to be detrimental to the solution. The mechanical stability of the system was assessed by comparing calibrations taken before and after ten months of routine camera system use. The results suggest sub-pixel interior orientation stability and millimetre-level relative orientation stability. Analyses of accuracy and parameter correlation demonstrate that a slightly-relaxed weighting strategy is preferred to tightly-enforced relative orientation stability constraints.
Open Access
Geometric Primitives in MLS Point Clouds Processing
(2020-04-14) Xia, Shaobo; Wang, Ruisheng; Lichti, Derek D.; Shahbazi, Mozhdeh M.; Gao, Yang; Kang, Zhizhong
Mobile Light Detection and Ranging (LiDAR), as an active remote sensing system, has become an accessible street-level mapping technology in the last decade due to its ability to collect accurate and dense 3D point clouds efficiently. Although tremendous effort has been made to LiDAR data processing, there still exist many problems in everyday tasks ( e.g., segmentation and detection). In this thesis, the LiDAR data processing is re-visited from a geometric-primitive perspective, with the hope that existing problems can be partly solved or even well addressed by tapping the potential of geometric primitives. A survey on geometric primitive extraction, regularization and their applications is presented for the first time. In this review, geometric primitives that consist of a group of discrete points are categorized into two classes: shape primitives (e.g., planes) and structure primitives (e.g., edges). The rest of this thesis focuses on geometric primitives in mobile LiDAR data processing. A fast 3D edge extraction method which consists of finding and linking edge candidates is proposed and tested in large-scale scenes. Given extracted edge clusters, a new facade separation method for mobile LiDAR point clouds is developed, based on which connected facades are separated into facade instances for the first time. To explore the potential of plane primitives in mobile LiDAR data processing, a novel instance-level building detection method based on plane primitives extracted from original point clouds is proposed. After that, a new point cloud segmentation algorithm that succeeds in separating buildings and vegetations is presented. The main contribution lies in using plane priors to improve segmentation accuracy. For line primitives, a new extraction method is presented in this thesis, which can extract multiple primitives simultaneously from projected point clouds. Based on extracted line segments, a graph-based method is presented to construct 2D building footprints. Last but not least, this thesis also introduces the energy-based ``hypothesis and selection" (HS) framework to object detection and segmentation in LiDAR point clouds for the first time. The adapted frameworks are proved to be flexible and effective according to extensive experiments in different applications.
Open Access
Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods
(MDPI, 2013-05-31) Chow, Jacky C. K.; Lichti, Derek D.; Glennie, Craig; Hartzell, Preston
Open Access
Improving Bluetooth-based Indoor Positioning Using Vision and Artificial Networks
(2020-07-09) Naghdi, Sharareh; O'Keefe, Kyle P. G.; Noureldin, Aboelmagd M.; Lichti, Derek D.; Liang, Steve H. L.; Barsocchi, Paolo
The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Global Navigation Satellite Systems (GNSS) receivers, while ubiquitous in outdoor positioning, are not effective indoors. One of the best solutions to solve this problem and increase the accuracy of the user's position in indoor areas is to apply other sensors. This research takes advantage of Bluetooth Low Energy (BLE) technology, vision systems, and Artificial Neural Networks (ANNs) to improve the accuracy of the position solutions in indoor environments for pedestrian applications. BLE technology faces challenges related to the Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This thesis presents methods to compensate for losses in the RSSI values by applying ANN algorithms to RSSI measurements from three BLE advertising channels. The resulting improved RSSI values are then converted into ranges using path loss models and trilateration is applied to obtain indoor positions. Two neural network algorithms were implemented. The first used only the RSSI values while the second incorporated a wearable camera as an additional source of information about the presence or absence of human obstacles. The results showed that the two proposed artificial-based systems could enhance RSSI due to human body shadowing and provide significantly better ranging and positioning solutions than fingerprinting and trilateration techniques with uncorrected RSSI values. Two proposed systems provided 3.7 m and 6.7 m positioning accuracy in 90 % of the time in a complex environment with the presence of the human body, nevertheless, the fingerprinting and the classic algorithms offered 8.7 m and 12.3 m position accuracy in the same situation, respectively.
Open Access
An Integrated Software Environment for Object-Based Cellular Automata: An Application to the Study of Land-Use Changes
(2019-09-12) Amini Tareh, Mahsa; Lichti, Derek D.; Lévy, Richard M.; Jacobson, Dan; Li, Songnian; Stefanakis, Emmanuel
Cellular automata (CA) is a well-established modelling approach used to study patterns and dynamics of land-use/land-cover (LULC) systems and to predict their evolution. Increased computer performance, along with the need to improve how geographic space is represented have resulted in the recent development of object-based CA models. Their main advantage over conventional cell-based models is that they allow for the representation of meaningful, real-world entities. However, their use remains limited due to issues with data model inconsistencies between calibration and simulation, simple neighborhood configurations and driving factors, overlooking spatial and temporal scaling effects on simulated results, increased computation time required to handle vector geometrical transformations and topology, and lack of an integrated framework that encompasses the functionalities required for calibration and simulation. The objective of this research is to describe the architecture and functionality of a novel, object-based CA model that were tested in two study areas in the Elbow River watershed in southern Alberta at 5 m and 60 m resolution. A change detection analysis is first performed on a series of historical LULC maps in vector format to identify the trends and speed of change in LULC and the driving factors responsible for these changes. This information is stored in a spatial database accessible from the software environment. Calibration is conducted with several neighborhood configurations and drivers using the multi-class weight of evidence method to calculate the transition probabilities. Simulation is performed by allowing for the change of state and geometry of each object over time. Time-consuming vector-handling operations are optimized or parallelized to increase the speed of execution. The final model results indicate a positive agreement with an independent map used for comparison. The model reproduces realistic urbanization patterns along the main roads and the river. Also, it is apparent that there is a substantial improvement in computation time. This model represents a powerful exploration and application tool that will enable a large community of users to exploit the potential of CA modelling for understanding the dynamics of LULC systems.
Open Access
A Model-based, Optimal Design System for Terrestrial Laser Scanning Networks in Complex Sites
(2019-08-29) Jia, Fengman; Lichti, Derek D.; O'Keefe, Kyle P. G.; Wang, Ruisheng; Shahbazi, Mozhdeh M.; Lindenbergh, Roderik C.
With the rapid increase of terrestrial laser scanner (TLS) applications, especially for the high-accuracy modelling of large-volume, complex objects, a design system is required to provide the optimal solutions for both scanner and target placement, so that the project requirements in terms of coverage, precision, economy and reliability can be met. In this thesis, a model-based, optimal design system for terrestrial laser scanning networks in complex sites is proposed. First, a hierarchical TLS viewpoint planning strategy driven by an improved optimization method is developed to solve the optimal scanner placement problem. The main contribution of the proposed method is to improve the efficiency in design without jeopardizing the optimality of the solution, compared with the traditional method with the extensive search strategy. In addition, the target placement for registration, which draws limited attention in the existing research, is determined by optimizing the target arrangement criterion, and the number of target locations is minimized by accepting the close to optimal target arrangement. Finally, the quality of the design, including the sensitivity of the object coverage to viewpoint placement and the precision of the point cloud are provided. The proposed methods were verified by the relatively small network first and then applied on two building complexes located on the University of Calgary campus. The design for scanner placement was compared with the “brute force” strategy in terms of the optimality of the solutions and runtime. The results showed that the proposed strategy provided scanning networks with a compatible quality but a significantly improved efficiency in design. The number of target locations necessary for registration from the proposed system was surprisingly small, considering the volume and complexity of the networks. Through the quality assessments, the sensitivity of the object coverage to the scanner placement indicated where users might need to consider viewpoint densification, and the point cloud precision indicated if the network design could meet the project requirements.
Open Access
Multi-camera panoramic imaging system calibration
(2019-01) Jarron, David; Lichti, Derek D.; Shahbazi, Mozhdeh M.; Radovanovic, Robert S.
A mobile mapping system (MMS) is a three-dimensional reality capture system that collects georeferenced spatial data with integrated navigation and imaging sensors from a moving vehicle. Several imaging subsystems can be found on board an MMS, such as panoramic camera systems and LiDAR sensors. The data collected from a panoramic imaging system must be accurately georeferenced and the sensors must be rigorously calibrated to ensure accurate registration of images to the point clouds collected by the LiDAR sensors, and to ensure panoramic images are generated seamlessly. The panoramic imaging system studied in this work is the Ladybug5 (by FLIR Integrated Imaging Solutions), which is a spherical camera system comprised of six individual wide-angle cameras. Having accurate estimates of the interior and relative orientation parameters of these cameras is essential for integrating the camera system with other sensors in the MMS to generate georeferenced spatial data. However, field experience has shown that factory-provided calibrations may be insufficiently accurate for high-precision applications. An investigation of the geometric calibration of the Ladybug 5 system was conducted in a dedicated indoor calibration facility at the University of Calgary: an 11 m x 11 m x 4 m field comprising 291 signalized photogrammetry targets. Multiple free-network, self-calibrating bundle adjustments were performed using different sets of constraints to model several systematic error sources. Weighted constraints were included in the adjustment to enforce the stability of the six relative orientation parameters between image pairs, and separate colour channel adjustments were used to compensate for chromatic aberrations. The overall fit of observations to the calibration model as measured by the root mean square error of the image point residuals was at the level of 0.3-0.4 pixels. Mean object point precision was at the 0.3 mm level. Rectified and ortho-rectified panoramas were also generated to verify the calibrations precision and observe how adjustments with constraints effect panorama generation.
Open Access
New approach for low-cost TLS target measurement
(ASCE, 2019-08) Lichti, Derek D.; Glennie, Craig L.; Jahraus, Adam; Hartzell, Preston J.
The registration and calibration of data captured with terrestrial laser scanner instruments can be effectively achieved using signalized targets comprising components of both high and low reflectivity, so-called contrast targets. For projects requiring tens or even hundreds of such targets, the cost of manufacturer-constructed targets can be prohibitive. Moreover, the details of proprietary target center co-ordinate measurement algorithms are often not available to users. This paper reports on the design of a low-cost contrast target using readily-available materials and an accompanying center measurement algorithm. Their compatibility with real terrestrial laser scanner data was extensively tested on six different instruments: two Faro Focus 3D scanners; a Leica HDS6100; a Leica P40; a Riegl VZ-400; and a Zoller+Fröhlich Imager 5010. Repeatability was examined as a function of range, incidence angle, sampling resolution, target intensity and target contrast. Performance in system self-calibration and from independent accuracy assessment is also reported. The results demonstrate compatibility for all five scanners. However, all datasets except the Faro Focus 3D require exclusion of observations made at high incidence angles in order to prevent range biases. Results also demonstrate that the spectral reflectivity of the target components is critical to ensure high contrast between target components and, therefore, high-quality target center co-ordinate measurements.
Open Access
Photogrammetric Modelling for 3D Reconstruction from a Dual Fluoroscopic Imaging System
(2019-01-03) Al Durgham, Kaleel Mansour; Lichti, Derek D.; Kuntze, Gregor; Wang, Ruisheng; Shortis, Mark R.; Boyd, Steven Kyle; Ronsky, Janet L.
Biplanar videoradiography (BPVR), or clinically referred to as dual fluoroscopy (DF), imaging systems are increasingly being used to study the in-vivo skeletal biomechanics of human and animal locomotion. DF imaging provides a novel solution to quantify the six-degree-of-freedom (6DOF) skeletal kinematics of humans and animals with high accuracy and temporal resolution. Using low-dose X-ray radiation, DF systems provide accurate bone rotation and translation measurements. In this research domain, a DF system comprises two X-ray sources, two image intensifiers and two high-speed video cameras. The combination of these elements allows for the stereoscopic imaging of the bones of a joint at high temporal resolution (e.g., 120-250 Hz), from which bone kinematics can be estimated. The utilization of X-ray-based imaging results in challenges that are uncommon in optical photogrammetry. Unlike optical images, the inherent lack of colour information in DF images complicates fundamental tasks such as the derivation of image observations for the system calibration. Furthermore, the incorporation of an image-intensifier to produce DF images results in high distortion artifacts that are uncommon in optical photogrammetry. The use of image intensifiers also results in non-uniform intensity response in the DF images. Unlike optical images with well-established camera models, the systematic distortion behaviour in DF images is empirically modelled. The novelty in this research work is in providing a complete, scientific, straightforward and accurate photogrammetric framework for deriving 3D measurements from a DF imaging system. This research work provides means for automating the DF calibration procedure and introduces solutions for improving the methodology of 3D reconstruction from DF imaging. A thorough photogrammetric analysis over the system aspects points out the weaknesses in the iii traditional 3D reconstruction procedures and suggests accurate alternatives. The dissertation presents five scientific contributions: (1) a semi-automated methodology to derive the image observations from time series DF calibration images, (2) validation of an empirical DF sensor model (bundle adjustment-based) for the calibration of the DF system and introducing it as a superior replacement for the traditional direct linear transformation-based (DLT) calibration approaches, (3) a rigorous accuracy assessment methodology for the evaluation of the DF system reconstruction capabilities, (4) a novel methodology for the temporal stability analysis of an imaging system calibration parameters, and (5) a virtual-3D-model means to facilitate establishing the alignment between stereoscopic DF image pair and an MRI/CT model (2D-to3D registration).
Open Access
Receiver-level Signal and Measurement Quality Monitoring for Reliable GNSS-based Navigation
(2019-01-09) Pirsiavash, Ali; Lachapelle, Gerard Jules; O'Keefe, Kyle P. G.; Broumandan, Ali; Lichti, Derek D.; Gao, Yang; Lohan, Elena-Simona
Global Navigation Satellite Systems (GNSS) are widely used in everyday and safety of life services as the main system for positioning and timing solutions. Reliability and service integrity are of utmost importance given a variety of error sources and threats. In the case of aviation and maritime applications, system integrity includes ground and space-based augmentation systems. These externally-aided monitoring systems do not provide a satisfactory solution for land users due to the multiplicity of error sources in the user's local environment, such as multipath. This research investigates receiver level stand-alone integrity monitoring solutions for such users. The methodology is based on Signal and Measurement Quality Monitoring (SQM and MQM) to detect and exclude or de-weight faulty measurements, with multipath and spoofing being the major concerns. Different monitoring metrics are defined and investigated for multipath detection and new geometry-based exclusion and de-weighting techniques are developed. Following an analytical discussion of metric sensitivity and effectiveness, simulated and field data analysis are provided to verify practical performance. Results obtained for the designed SQM and MQM-based detection metrics show reliable performance of 3 to 5 m Minimum Detectable Multipath Error (MDME). Although limited by multipath characteristics and measurement geometry, when detected faulty measurements are excluded or de-weighted, positioning performance improves for various multipath scenarios. In order to effectively classify multipath and spoofing, a spoofing simulator is designed, implemented and tested for selected time and position spoofing scenarios. A new spoofing strategy is described to investigate the minimum number of satellite signals required for an effective spoofing attack. Results show that in an overlapped spoofing scenario, at least 60% of signals are spoofed and thus distorted. This rate of signal distortion is not the case in all but harsh multipath scenarios and is used to distinguish spoofing attacks from multipath. More importantly, it is shown that distortion of more than half of the signals makes position solutions unreliable regardless of the error source. For selected scenarios, two-dimensional time/frequency widely-spaced SQM metrics are also developed to detect spoofing signals with about 3% false alarm probability imposed by multipath and other sources of signal distortion.
Open Access
The Use of Reality Capture Technologies to Mediate Relocation Impacts: A Case Study at the Perrenoud Homestead Provincial Historic Resource, Alberta
(2020-05-11) Hvidberg, Madisen; Dawson, Peter C.; Oetelaar, Gerald A.; Lichti, Derek D.
Relocation of buildings has been a common practice for centuries and is now frequently used as a means to preserve heritage structures in the face of various economic, social, and environmental risks. In relocating a heritage structure, documentation is of the utmost importance because of the adverse effects that relocation can have. Reality capture technologies provide a powerful tool for rapidly recording real-world phenomena in three-dimensions but have yet to be utilized for the documentation needs of relocation projects. This thesis provides a novel example of these technologies used for not only documentation of, but in an assessment of the impacts of relocation at the Perrenoud Homestead Provincial Historic Resource (PHR). During its disassembly, the Perrenoud Homestead was digitally documented using terrestrial LiDAR (laser scanning) and drone-based photogrammetry. The resulting datasets were then used to explore the impacts of relocation to the structural integrity of the site, through a three-part analysis of visual inspection, angular measurements, and change detection. A discussion was then posed about the consequences of the project on the commemorative integrity of the site, looking at dynamics of reality capture and the physical components of the PHR, as well as changes to the visitor experience and accessibility of this site. Overall, this thesis presents an example of the benefits of reality capture technologies to heritage relocation projects, and advocates for more incorporation of these methods for similar initiatives in the future.
Open Access
Wide-angle Lens Camera Calibration using Automatic Target Recognition
(2020-05-15) Jarron, David Mackenzie; Lichti, Derek D.; Shahbazi, Mozhdeh M.; O'Keefe, Kyle P. G.; Detchev, Ivan D.
The focus of this thesis is the calibration and integration of the Ladybug5 multi-camera system into a Mobile Mapping System. To calibrate this system an efficient and accurate automatic target recognition methodology that could work with a multi-camera system was needed. This automatic target recognition methodology was developed and works by projecting the known coordinates of the surveyed calibration targets into the camera frame through a series of simulated or measured orientations and matching to signalized targets already detected in the image to a very high degree of accuracy. Through calibration of this system and rigorous modelling of its intrinsic properties, it became apparent that there was ambiguity in the research field about the most precise projection model to use for wide angle lens cameras and camera systems. A series of camera calibrations were carried out on two wide angle camera systems. Both camera systems exhibit properties that make them difficult to classify as either a central perspective camera or as a fisheye camera. Calibrations were performed on both camera systems using both central perspective and fisheye projection models. The calibrations that utilized a fisheye projection model estimated calibration parameters that more closely fit the observations. Finally, the calibration of the Ladybug5 as a multi-camera system, utilizing ROP stability constraints was performed to rectify issues relating to issues with the panoramic image generation of the Ladybug5. These panoramic images are important for point cloud coloration, and other aspects of multi-camera system integration with mobile mapping systems. It was determined that the calibration of the Ladybug5 using relative orientation stability constraints allowed for the generation of more seamless panoramic images, allowing the camera to better integrate with mobile mapping systems.

Browsing by Author "Lichti, Derek D."

Results Per Page

Sort Options

Libraries & Cultural Resources