Mobile Laser Scanning Research Articles

A Railway Lidar Point Cloud Reconstruction Based on Target Detection and Trajectory Filtering

The traditional railway survey adopts a manual observation method, such as a total station measuring system. This method has high precision, but the amount of data is small, and the measurement efficiency is low. Manual measurement cannot meet the requirements of dynamic continuous high-precision holographic measurement during railway outages. Mobile laser scanning is a mobile mapping system based mainly on a laser scanner, inertial measurement unit (IMU) and panoramic camera. Mobile laser scanning has the advantages of high efficiency, high precision and automation. However, integrating inertial navigation data and mobile laser scanning data to obtain real 3D information about railways has always been an urgent problem to be solved. Therefore, a point cloud reconstruction method is proposed based on trajectory filtering for a mobile laser scanning system. This paper corrects the odometer data by identifying railway feature points through deep learning and uses Rauch–Tung–Striebel (RTS) filtering to optimize the trajectory results. Combined with the railway experimental track data, the maximum difference in the east and north coordinate direction can be controlled within 7 cm, and the average elevation error is 2.39 cm. This paper applies a multi-sensor integrated mobile detection system to railway detection. It is of great significance to the healthy development of the intelligent railway system.

Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion

Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments.

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection.

A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds

Urban management and survey departments have begun investigating the feasibility ofacquiring data from various laser scanning systems for urban infrastructure measurements and assessments. Roadside objects such as cars, trees, traffic poles, pedestrians, bicycles and e-bicycles describe the static and dynamic urban information available for acquisition. Because of the unstructured nature of 3D point clouds, the rich targets in complex road scenes, and the varying scales of roadside objects, finely classifying these roadside objects from various point clouds is a challenging task. In this paper, we integrate two representations of roadside objects, point clouds and multiview images to propose a point-group-view network named PGVNet for classifying roadside objects into cars, trees, traffic poles, and small objects (pedestrians, bicycles and e-bicycles) from generalized point clouds. To utilize the topological information of the point clouds, we propose a graph attention convolution operation called AtEdgeConv to mine the relationship among the local points and to extract local geometric features. In addition, we employ a hierarchical view-group-object architecture to diminish the redundant information between similar views and to obtain salient viewwise global features. To fuse the local geometric features from the point clouds and the global features from multiview images, we stack an attention-guided fusion network in PGVNet. In particular, we quantify and leverage the global features as an attention mask to capture the intrinsic correlation and discriminability of the local geometric features, which contributes to recognizing the different roadside objects with similar shapes. To verify the effectiveness and generalization of our methods, we conduct extensive experiments on six test datasets of different urban scenes, which were captured by different laser scanning systems, including mobile laser scanning (MLS) systems, unmanned aerial vehicle (UAV)-based laser scanning (ULS) systems and backpack laser scanning (BLS) systems. Experimental results, and comparisons with state-of-the-art methods, demonstrate that the PGVNet model is able to effectively identify various cars, trees, traffic poles and small objects from generalized point clouds, and achieves promising performances on roadside object classifications, with an overall accuracy of 95.76%. Our code is released on https://github.com/flidarcode/PGVNet.

3D Point Cloud Generation Based on Multi-Sensor Fusion

Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels.

Mobile Laser Scanning for Estimating Tree Structural Attributes in a Temperate Hardwood Forest

The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS also shows great potential for estimating inventory attributes that are difficult to measure in the field, such as wood volume or crown dimensions. Hardwood species represent a significant challenge for wood volume estimation compared to softwoods because a substantial portion of the volume is included in the crown, making them more prone to allometric bias and more complex to model. This study assessed the potential of MLS data to estimate tree structural attributes in a temperate hardwood stand: height, crown dimensions, diameter at breast height (DBH), and merchantable wood volume. Merchantable wood volume estimates were evaluated to the third branching order using the quantitative structural modeling (QSM) approach. Destructive field measurements and terrestrial laser scanning (TLS) data of 26 hardwood trees were used as reference to quantify errors on wood volume and inventory attribute estimations from MLS data. Results reveal that SLAM-based MLS systems provided accurate estimates of tree height (RMSE = 0.42 m (1.78%), R2 = 0.93), crown projected area (RMSE = 3.23 m2 (5.75%), R2 = 0.99), crown volume (RMSE = 71.4 m3 (23.38%), R2 = 0.99), DBH (RMSE = 1.21 cm (3.07%), R2 = 0.99), and merchantable wood volume (RMSE = 0.39 m3 (18.57%), R2 = 0.95), when compared to TLS. They also estimated operational merchantable volume with good accuracy (RMSE = 0.42 m3 (21.82%), R2 = 0.94) compared to destructive measurements. Finally, the merchantable stem volume derived from MLS data was estimated with high accuracy compared to TLS (RMSE = 0.11 m3 (8.32%), R2 = 0.96) and regional stem taper models (RMSE = 0.16 m3 (14.7%), R2 = 0.93). We expect our results would provide a better understanding of the potential of SLAM-based MLS systems to support in-situ forest inventory.

Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest

National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction. The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%. Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated.

Automatic Tunnel Crack Inspection Using an Efficient Mobile Imaging Module and a Lightweight CNN

Cracks in tunnel linings are the most common tunnel defects. As early indicators of structural deterioration, cracks represent critical problems for the safety of tunnels. Several mobile tunnel inspection systems (MTISs) have been developed for tunnel crack inspection. However, due to the weak signals of cracks, these MTISs require considerable exposure time to capture high-quality tunnel images, necessitating a low travel speed. Meanwhile, traditional crack detection methods encounter difficulties in processing tunnel crack images because of their low contrast and poor continuity. To overcome these challenges, this study presents a new MTIS for fast tunnel crack inspection that consists of a novel mobile imaging module and an automatic crack detection module. The imaging module is composed of an array of high-resolution charge-coupled device (CCD) cameras, a mobile laser scanner, and a lighting array. The core of the crack detection module is a novel lightweight convolutional neural network (CNN) designed for efficient tunnel crack detection, with an effective spatial constraint strategy to guarantee crack continuity. We collected a new tunnel crack dataset consisting of 1,218 images using our mobile imaging module at a driving speed of 80 km/h. Comprehensive experiments were conducted on this dataset to evaluate the performance of our proposed network. The results demonstrate that the presented CNN can effectively detect tunnel cracks with state-of-the-art performance, achieving an F1-score greater than 0.88 and an inference speed of 17 FPS with only 3.4M model parameters. The code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/urban-informatics/LinkCrack</uri> .

A self-attention based global feature enhancing network for semantic segmentation of large-scale urban street-level point clouds

• A novel network for semantic segmentation of large-scale urban point clouds. • A self-attention-based channel-wisely enhanced global feature is constructed. • A weighted semantic mapping module for highly accurate semantic segmentation. Point clouds of large-scale urban street scenes contain large quantities of object categories and rich semantic information. The semantic segmentation is the basis and key to subsequent essential applications, such as digital twin engineering and city information model. The global feature of point clouds in large-scale scenes can provide long-range context information, which is critical to high-quality semantic segmentation. However, the learning of global spatial saliency considering class label constraints is often ignored in the feature representation of some deep learning models. With regard to this, we propose a Global Feature Self-Attention Encoding (GFSAE) module and a Weighted Semantic Mapping (WSM) module to make the semantic segmentation model of point clouds in large-scale urban street scene focus more on the global salient feature expression by self-attention enhancement channel by channel and take into account the constraints of semantic categories to learn a better semantic segmentation model for urban street scenes. The experiments are performed on the Semantic3D dataset and our own collected vehicle Mobile Laser Scanning (MLS) point cloud dataset. The segmentation results show that the GFSAE and the WSM proposed by us can improve the semantic segmentation of point clouds in large-scale urban street scenes and prove the effectiveness of our model compared with other state-of-the-art methods.

A Study of Point Laser for Automatic Local Road Condition Assessment: Feasibility, Challenges, and Limitations

Road condition assessment plays an important role in maintaining the integrity of mature and fast-expanding road networks. Such assessments provide invaluable information for local authorities that enable them to deliver timely preventative road network maintenance. To enable the best use of scarce maintenance resources, it is necessary to assess the condition of the roads periodically. In this article, we present the Point Laser (PL) system as an alternative approach for automatic road assessment. Compared to current systems, ground penetrating radar and the mobile laser scanning system, the PL system has the advantages of low hardware cost, low computational requirements, and fewer environmental restrictions in relation to its use. The article first outlines the principle of the PL system including a low-complexity signal processing approach. Afterward, the feasibility, challenges, and limitations of the PL system are discussed based on its use in a real environment. A visual-based road survey is used as a ground truth to determine the PL’s performance in relation to the requirements of road condition assessment at various levels of road management. The results of the comparison demonstrate that the PL system is an ideal candidate for the automatic assessment of the condition of local road networks, especially those that require frequent assessment for road asset management.

Assessing the effects of burn severity on post-fire tree structures using the fused drone and mobile laser scanning point clouds

Wildfires burn heterogeneously across the landscape and create complex forest structures. Quantifying the structural changes in post-fire forests is critical to evaluating wildfire impacts and providing insights into burn severities. To advance the understanding of burn severities at a fine scale, forest structural attributes at the individual tree level need to be examined. The advent of drone laser scanning (DLS) and mobile laser scanning (MLS) has enabled the acquisition of high-density point clouds to resolve fine structures of individual trees. Yet, few studies have used DLS and MLS data jointly to examine their combined capability to describe post-fire forest structures. To assess the impacts of the 2017 Elephant Hill wildfire in British Columbia, Canada, we scanned trees that experienced a range of burn severities 2 years post-fire using both DLS and MLS. After fusing the DLS and MLS data, we reconstructed quantitative structure models to compute 14 post-fire biometric, volumetric, and crown attributes. At the individual tree level, our data suggest that smaller pre-fire trees tend to experience higher levels of crown scorch than larger pre-fire trees. Among trees with similar pre-fire sizes, those within mature stands (age class: &amp;gt; 50 years) had lower levels of crown scorch than those within young stands (age class: 15—50 years). Among pre-fire small- and medium-diameter trees, those experiencing high crown scorch had smaller post-fire crowns with unevenly distributed branches compared to unburned trees. In contrast, pre-fire large-diameter trees were more resistant to crown scorch. At the plot level, low-severity fires had minor effects, moderate-severity fires mostly decreased tree height, and high-severity fires significantly reduced diameter at breast height, height, and biomass. Our exploratory factor analyses further revealed that stands dominated by trees with large crown sizes and relatively wide spacing could burn less severely than stands characterized by regenerating trees with high crown fuel density and continuity. Overall, our results demonstrate that fused DLS-MLS point clouds can be effective in quantifying post-fire tree structures, which facilitates foresters to develop site-specific management plans. The findings imply that the management of crown fuel abundance and configuration could be vital to controlling burn severities.

LiDAR intensity correction for road marking detection

The intensity data recorded by light detection and ranging (LiDAR) contains the spectral characteristics of a scanned target, making it an essential source of information in mobile laser scanning (MLS) related applications. However, the raw intensity can be distorted by distance and incidence angle, obstructing its use in autonomous driving. To correct the intensity distortion, an automatic demarcation selection, based on distance effect function, and a Blinn-Phong model-based angle effect correction, have been proposed to correct the raw intensity, establishing the relationship between intensity correction performance and the influencing factors, including modeling data selection, incidence angle span (IAS) and point cloud density. Separability (SEP) and balanced F score (BFS) were used to evaluate the separability, accuracy and totality of road marking detection. Experimental results showed that the background data-driven angle effect function (BDAF) used was beneficial for enhancing marking and road classification. IAS has a major influence on correcting intensity performance. SEP was improved by 35% when IAS was increased from 3.17° to 13.5°, with point density of 535 pt/m2. The IAS threshold could be adjusted according to the instrument performance used. It offers a meaningful investigation of detection and classification of planar target using corrected MLS intensity.

BIM-supported scan and flight planning for fully autonomous LiDAR-carrying UAVs

The Unmanned Aerial Vehicle (UAV) equipped with a Light Detection and Ranging (LiDAR) scanner provides a versatile and efficient platform for mobile laser scanning in construction-related scenarios. Recent studies on UAVs have demonstrated point-to-point navigation, yet there has been sparse investigation on scan coverage planning to fully explore a construction site, and on kinodynamic motion planning to ensure energy-efficient trajectories. This study develops a Building Information Model (BIM)-supported framework to facilitate scan planning and motion planning of autonomous LiDAR-carrying UAVs. The proposed framework selectively integrates the geometry and semantics from BIM to construct a probabilistic 3D voxel map. Then, a greedy algorithm is developed to iteratively generate waypoints with optimized coverage. After that, a collision-free guiding path is computed for traversing all the waypoints before it is further transformed into a high-degree polynomial trajectory. The proposed framework was validated in a simulated construction scenario of water treatment facilities using MATLAB and Unreal Engine 4 (UE4). The planned trajectory demonstrated smoothness, energy efficiency, and sufficient coverage. It reduced 86.17% of required moments from motors over the regular A* algorithm and achieved 91.67% scan coverage on the target facility.

A geometry-aware attention network for semantic segmentation of MLS point clouds

Semantic segmentation of mobile laser scanning (MLS) point clouds can provide meaningful 3 D semantic information of urban facilities for various applications. However, it still remains a challenge to extract accurate 3 D semantic information from MLS point cloud data due to its irregular 3 D geometric structure in a large-scale outdoor scene. To this end, this study develops a geometry-aware attention point network (GAANet) with geometric properties of the point cloud as a reference. Specifically, the proposed method first builds a graph-like region for each input point to establish the geometric correlation toward its neighbors for robustly descripting local geometry-aware features. Thereafter, the method introduces a novel multi-head attention mechanism to efficiently learn local discriminative features on the constructed graphs and a feature combination operation to capture both local and global geometric dependencies inside fused point features for significantly facilitating the segmentation of small or incomplete 3 D objects at point-level. Finally, an adaptive loss function is appended to handle class imbalance for the overall performance improvement. The validation experiments on two challenging benchmarks demonstrate the effectiveness and powerful generation ability of the proposed method, which achieves state-of-the-art performance with mean IoU of 65.09% and 95.20% in the Toronto-3D and Oakland 3-D MLS dataset, respectively.

Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure

Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%.

Comparing tree attributes derived from quantitative structure models based on drone and mobile laser scanning point clouds across varying canopy cover conditions

Complex canopy cover conditions often challenge the accurate measurement of many individual tree attributes that are pivotal to the sustainable management of forest resources. Advances in drone laser scanning (DLS) and mobile laser scanning (MLS) have enabled the acquisition of high-density point clouds with the potential to better resolve detailed tree structures. Yet, the quality of DLS and MLS data can be limited by occlusions and environmental complexities. To quantify the impacts of canopy cover on the tree attribute estimation, this study investigated the utility of DLS and MLS data both individually and combined. Considering the scanning characteristics, we examined direct fusion and a new strategy using a relative weighting scheme based on the probability density of vertical point distribution. We compared the accuracy of seven tree attributes derived from quantitative structure models (QSMs) based on (1) DLS, (2) MLS, (3) fused, and (4) weighted point clouds under low, moderate, and high canopy cover levels. We found that the weighted data improved the modelling efficiency of QSMs by ∼ 20% on average, compared to fused and MLS data. Across canopy cover levels, the fused and weighted data achieved comparable results and outperformed DLS/MLS data in estimating tree attributes. Specifically, diameter at breast height and crown base height were accurately extracted from the fused, weighted, and MLS data under low canopy cover with the concordance correlation coefficient (CCC) > 0.80. As canopy cover increased, they were best estimated using the fused data (CCC > 0.90, RRMSE < 22%). Height was accurate regardless of canopy cover, which was independent of data collection platforms (CCC > 0.80, RRMSE < 16%). The crown diameter was also well estimated by fused, weighted, and MLS data across canopy cover levels (CCC > 0.82, RRMSE < 19%). The total, stem, and branch volumes could be best modelled by the fused data with increasing canopy cover. Overall, the fusion of DLS and MLS point clouds allowed the retrieval of comprehensive tree-level information. However, forestry practitioners still need to evaluate the trade-offs in selecting the most appropriate platform for laser scanning data based on their needs. Future studies should also enhance the modelling of trees with complex branching structures to strengthen the extraction of diverse attributes.

Geometric and radiometric constraints-based extraction of urban road manhole covers and their maintenance-related information using mobile laser scanning data

In recent years, mobile laser scanning (MLS) has attracted increasing interest for road network three-dimensional mapping, where one of the applications of MLS data is automation in urban road manhole covers detection and their information measurement for monitoring and management of manhole covers on regular-basis. Therefore, a multi-step method is proposed based on geometric and radiometric constraints on manhole covers for their extraction and maintenance-related information measurement. The proposed method was implemented using graphics processing unit (GPU)-based parallel computing framework with improved computation time. The method performance was evaluated using MLS datasets of two complex urban roadway test sites, which included partially overlapping objects, occluded road surfaces, and intra-class variability. The manhole covers were extracted at average completeness, correctness and quality of 96.69%, 98.32%, and 95.12%, respectively. The proposed method performed satisfactorily in the challenging cases of road manhole covers and achieves significant improvement in comparison with several state-of-the-art methods.

Analysis of SLAM-Based Lidar Data Quality Metrics for Geotechnical Underground Monitoring

Adverse ground behavior events, such as convergence and ground falls, pose critical risks to underground mine safety and productivity. Today, monitoring of such failures is primarily conducted using legacy techniques with low spatial and temporal resolution while exposing workers to hazardous environments. This study assesses the potential of novel simultaneous localization and mapping (SLAM)-based light detection and ranging (Lidar) data quality for rapid, digital, and eventually autonomous mine-wide underground geotechnical monitoring. We derive a comprehensive suite of quality metrics based on tests in two underground mines for two state-of-the-art mobile laser scanning (MLS) systems. Our results provide evidence that SLAM-based MLS provides data of the quality required to detect geotechnically relevant changes while being significantly more efficient for large mine layouts when compared to traditional static systems. Additionally, we show that SLAM-specific processing can achieve an order of magnitude better relative accuracy relevant for change detection than quality metrics derived from traditionally deployed tests would suggest while reducing SLAM drift error by up to 90%. In collaboration with an operating block cave mine, we confirm these capabilities in field tests on a mine-wide scale and, for the first time, demonstrate methods of rockfall detection using MLS data. While more work is required to investigate optimal collection, processing, and utilization of MLS data, we demonstrate its potential to become an effective and widely applicable data source for rapid, accurate, and comprehensive geotechnical inspections.

Optimal scan planning for surveying large sites with static and mobile mapping systems

Since the last two decades, the use of laser scanners for generating accurate and dense 3D models has been rapidly growing in multiple disciplines. The reliance on human-expertise to perform an efficient scanning in terms of completeness and quality encouraged the researchers to develop strategies for carrying out an optimized and automated scan planning. Nevertheless, due to the predominant use of static terrestrial laser scanners (TLS), the most of developed methods have been focused on scan optimization by fixing standpoints on basis of static scanning. The increasing use of portable mobile laser scanning systems (MLS) enables faster non-stop acquisition which demands the planning of optimal scan trajectories. Therefore, a novel method addressing the absence of dynamic scan planning is proposed considering specific MLS constraints such as maximum acquisition time or closed-loops requirement. First, an initial analysis is carried out to determinate key-positions to reach during data acquisition. From these positions a navigable graph is generated to compute routes satisfying specific MLS constraints by a three-step process. This starts by estimating the number of routes necessary to subsequently carry out a coarse graph partition based on Kmedoids clustering. Next, a balancing algorithm was implemented to compute a balanced graph partition by node exchanging. Finally, partitions are extended by adding key nodes from their adjacent ones in order to provide a desirable overlapping between scans. The method was tested by simulating three laser scanner configurations in four indoor and outdoor real case studies. The acquisition quality of the computed scan planning was evaluated in terms of 3D completeness and point cloud density with the simulator Helios++.