Symmetric Star-convex Shape Tracking With Wishart Filter

Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%.

Light Detection and Ranging (LiDAR) systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality control (QC) of LiDAR data has not followed the same trend. In other words, a lack in reliable, practical, cost-effective, and commonly acceptable QC procedures is evident. A frequently adopted procedure for QC is comparing the LiDAR data to ground control points. Aside from being expensive, this approach is not accurate enough for the verification of horizontal accuracy, unless specifically designed LiDAR targets are used. This paper is dedicated to providing accurate, economical, and convenient internal QC procedures for the evaluation of LiDAR data, which is captured from parallel flight lines. The underlying concept of the proposed methodologies is that, in the absence of systematic and random errors in system parameters and measurements, conjugate surface elements in overlapping strips should perfectly match each other. Consistent incompatibilities and the quality of fit between conjugate surface elements in overlapping strips can be used to detect systematic errors in the system parameters/measurements and to evaluate the noise level in the LiDAR point cloud, respectively. Experimental results from real data demonstrate that all the proposed methods, with one exception, produce compatible estimates of systematic discrepancies between the involved data sets, as well as good quantification of inherent noise.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical & digital imagery information for various applications.

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 El Mayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

National and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average F 1 score = 96%), closely followed by the hybrid method (average F 1 score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average F 1 score = 82%) and depended on landscape context and vegetation composition (e.g. the F 1 score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.

A second-generation Gamma Remote Sensing Portable RAdio Detection and Ranging (RADAR) Interferometer (GPRI-II) and a Lieca C-10 LIght Detection and Ranging (LIDAR) were employed to monitor the volume change behavior of two thin compacted clay test sections at the University of Arkansas Cato Springs Research Center (UACRSC). The viability of using remote sensing methods to accurately identify and quantify deformation associated with expansive clay behavior was evaluated. Two 464.52 m2 (5,000 ft2) sections consisting of 20.3 cm (8 inches) thick compacted clay layer underlain by a 5.1 cm (2 inches) thick sand drainage blanket were constructed. One test section was amended with three (Phase 1) and six (Phase 2) percent sodium bentonite. Four LIDAR and 355 RADAR observations were captured from the roof of the UACSRC building between January and July, 2012. Multiple RADAR images were captured on a weekly basis and after significant precipitation events. RADAR measurements, obtained by calculating the interferometric phase difference between coregistered temporally separated RADAR images, were used to determine topography and deformation. LIDAR measurements, as obtained from LIDAR point clouds, were also used to determine topography and deformation. To obtain accurate topography and deformation measurements the vegetation effects within the LIDAR data was removed. Deformation maps generated using both the RADAR and LIDAR methods are presented and compared. The results indicate both technologies are capable of detecting surface movements associated with expansive clay behavior. Moreover, the surface movements associated with expansive clay behavior can be accurately quantified using the LIDAR device.

LiDAR (Light Detection and Ranging) is a positive remote sensing technology. In this paper, with analyzed the characteristic of LiDAR data and discussed the advantages of LiDAR data, we present an improved data filtering algorithm for LiDAR data. It is basing on the last return laser pulses and tries to design an fast pre-processing LiDAR point data with the purpose of DEM Modeling from LiDAR based point cloud data. The algorithm contains two times of filtering with different aims and data process strategies. Experiments of real terrain area are presented for testing efficiency and accuracy of this filtering method.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

The continuous and reliable operation of subsea pipelines is of critical importance to the global oil and gas industry, as these pipelines are responsible for transporting vast quantities of hydrocarbons from offshore production fields to onshore facilities. However, the harsh underwater environment in which these pipelines operate presents numerous challenges, including exposure to corrosive seawater, extreme pressure variations, and mechanical stress from geological movements. These factors can cause significant structural degradation over time, leading to potential corrosion, material fatigue, and deformation. Without regular monitoring, such issues may go undetected, increasing the risk of leaks, spills, or catastrophic failures, which would have severe environmental, economic, and safety consequences. Traditionally, subsea pipeline monitoring has relied on periodic inspections using technologies such as remotely operated vehicles (ROVs), sonar, and visual assessments. While these methods have proven useful, they often suffer from limitations such as low resolution, infrequent data collection, and the high operational costs associated with deploying ROVs and divers for manual inspections. Moreover, these traditional methods may not be capable of providing the continuous, real-time data needed to make proactive maintenance decisions and ensure pipeline integrity. This research paper focuses on the application of LiDAR (Light Detection and Ranging) technology as a transformative solution for monitoring the structural health of subsea pipelines. LiDAR is a remote sensing technology that uses laser pulses to capture detailed, high-resolution 3D data about surfaces and environments. By adapting LiDAR for underwater use, this paper proposes a novel method for real-time, continuous monitoring of pipeline conditions. The use of LiDAR in subsea environments presents unique opportunities to detect early signs of corrosion, material degradation, and structural deformation with a level of precision that is difficult to achieve with conventional monitoring techniques. The key advantage of LiDAR technology lies in its ability to collect accurate and comprehensive data in challenging conditions. Underwater LiDAR systems can penetrate turbid water, providing clear images and measurements of pipeline surfaces, even in low-visibility environments. The resulting high-resolution 3D models allow for the detailed analysis of pipeline integrity, enabling operators to track changes in pipeline conditions over time and respond to potential issues before they escalate. This capability is particularly important for detecting corrosion and deformation, which can occur gradually and may not be immediately apparent using traditional methods.In this paper, we explore the current state of subsea pipeline monitoring technologies, comparing the strengths and limitations of existing approaches. We then introduce a LiDAR-based solution, discussing the technical adaptations necessary for using LiDAR in subsea conditions. This includes considerations for data acquisition, processing, and analysis, as well as the challenges posed by underwater environments, such as light absorption and scattering by water particles. Furthermore, mathematical models for calculating corrosion rates and predicting deformation based on LiDAR data will be presented. These models allow for quantitative assessments of the structural health of pipelines, enabling operators to make informed decisions about maintenance schedules and interventions. The use of real-time data not only improves the accuracy of these models but also provides the ability to forecast future pipeline behavior, reducing the likelihood of unexpected failures. This paper also includes case studies and diagrams to demonstrate the practical application of LiDAR in subsea pipeline monitoring. By presenting real-world examples of how this technology has been implemented, we aim to highlight its effectiveness in improving the safety and longevity of critical infrastructure in the oil and gas sector. LiDAR technology represents a significant advancement in the field of subsea pipeline monitoring, offering enhanced data accuracy, real-time monitoring capabilities, and cost-effective solutions for ensuring pipeline integrity. This research paper not only outlines the potential of LiDAR as a monitoring tool but also provides a roadmap for its implementation in the oil and gas industry, with the ultimate goal of reducing risks, minimizing environmental impacts, and ensuring the continued safe operation of subsea pipelines.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this paper reports a fundamental study that employs a previously developed novel wind tunnel testing methodology to investigate and quantify the effects of stress factors affecting LiDAR perception. It was found that soiling characteristics, such as raindrop size distribution and droplet impact kinematics, and material properties, such as surface roughness and wettability, all play critical roles in influencing LiDAR performance to different extents. The results suggest that although LiDAR is an optical type of sensor, its perception does not necessarily align with camera vision, which is closer to human perception. Specifically, hydrophilic surfaces show better advantages over hydrophobic surfaces for LiDAR sensor applications when driving in rain. The overall relationships between material properties, adherent raindrop dynamics, and LiDAR perceptions are summarized.&lt;/div&gt;&lt;/div&gt;

Background The creation of defensible space, an area around the house where fuel is managed according to specific guidelines, can reduce ignition and fire spread, mitigating wildfire threat to communities. Remote sensing approaches can facilitate assessments of defensible spaces. Aims To develop a protocol to evaluate which defensible space hazards can be accurately assessed using Light Detection and Ranging (LiDAR). Methods We used high and low point density airborne LiDAR to measure hazards in 31 gardens in southeast Tasmania (Australia) based on alignment with defensible space guidelines. We compared the resulting ‘garden hazard scores’ with those generated from field observations to identify garden characteristics that can be estimated using LiDAR, as well as the best analysis resolution to do so. Key results We found a good association between field data and LiDAR measures of vegetation cover and spatial arrangement, but not plant flammability, inferred using Normalised Difference Vegetation Index (NDVI), or flammable objects. Low-density LiDAR consistently underestimated cover metrics compared with High-density LiDAR. Nonetheless, garden hazard scores calculated using either high- or low-density LiDAR yielded results similar to those obtained using only field observations. Implications LiDAR can be used to conduct semi-automatic, large-scale measurements of some, albeit not all, critical garden hazards.

This chapter introduces the data processing methods for two typical robotic perception systems: light detection and ranging (LIDAR)-based and Flash LIDAR Camera (FLC)-based systems. The most popular 3D perception sensors in robotics include stereovision system and light detection and ranging (LIDAR). The representative applications of stereovision have been demonstrated in NASA's Mars Exploration Rover [4, 5] program and the DARPA's Learning Applied to Ground Robots [6–9] program. A LIDAR system outperforms a stereovision system in terms of the completeness and accuracy of range data. An FLC illuminates the entire scene with a single laser pulse (or modulated infrared light) and focuses the image onto the 3D sensor's focal plane array (FPA). The sensors used for mapping and navigation include a 2D LIDAR and a three-axis fiber optic gyro. A robotic device is a portable robotic system with navigational functions such as positioning, obstacle detection, and scene recognition.

Green infrastructures (GI) are key elements in urban areas for heat mitigation, carbon capture and providing of aesthetic reasons. However, there is currently limited knowledge about the effects of various plant compositions, arrangements and varying density of plant cover, because traditional measuring methods are expensive / labour-intensive, imprecise, and tall buildings pose accessibility challenges. The presented study proposes applying LiDAR measurements on GI to gain in-depth understanding of plant growth, inventory of vegetation cover and thereby providing a useful tool for sustainable urban hazard management.The use of LiDAR (Light Detection and Ranging) technology has revolutionised forest monitoring by offering precise, efficient, and highly detailed spatial data for creating comprehensive 3D reconstructions of forest structures. The ability to capture fine details on both vegetation and structural surfaces is particularly advantageous for studying complex, vertical environments such as green fa&amp;#231;ades. This study used static ground-based LiDAR (RIEGL VZ-600i) to capture the 3D structure of a vertical greenery with wooden support structures before and after harvesting. Defined squares of 1 m&amp;#178; were fully harvested, the biomass collected and dry weight was obtained. Reference measurements for vegetation height (distance from wall to the outermost part of the plant) were recorded on a grid for 40 measurement points. The reference measurements were related to LiDAR alpha-hull volumetric analysis and predictions of growing biomass could be derived.By integrating point cloud analysis developed for forest monitoring into urban contexts, LiDAR facilitates a holistic analysis of natural and built environments. By analysis of LiDAR intensity and mapping further reference measurements for plant vitality and structural integrity, green wall health can be evaluated. Already established practices like alpha-hulling provide a successful tool to document green fa&amp;#231;ades comprehensively. Combining LiDAR with traditional measures enhances our understanding of the interactions between vegetation and architectural surfaces, enabling improved design and maintenance of GI and NBS to enable better planning and maintaining of NBS to reduce the effect of urban heat islands.&amp;#160;

The concept of the laser guide star was proposed by Foy and Labeyrie in 1985. The Laser Guide Star (LGS) depends on the abundance and distribution of sodium in the mesosphere. The mesospheric sodium often appears to consist of more than one layer, each of which exhibits time dependent variations in density and altitude. The non-zero thickness and finite range of the layers results in elongation of the LGS image resulting in a LGS defocus on extremely large telescopes such as TMT and VLT. Three methods have been proposed to measure the focus changes: direct imaging of the sodium plume, LIDAR (Light Detection and Ranging), and averaging of the LGS WFS focus error over 30-60 s. Direct imaging of the plume requires an additonal telescope. The principal advantage of LIDAR is the accurate height measurement. With LGS WFS focus error averaging, there are problems with the initial focus setting. Sodium resonance-fluorescence LIDAR has been undertaken by a number of groups: Purple Crow LIDAR (University of Western Ontario), Colorado State University LIDAR, and UBC (University of British Columbia) LIDAR. A number of means have been developed by TMT, SOR and VLT to correct Na variability. Na variability will be examined for its determination, analysis and impact on AO aberation correction.