Multiple Light Detection And Ranging Research Articles

Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

Automatic multi-LiDAR calibration and refinement method

Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.

Automated Method for SLAM Evaluation in GNSS-Denied Areas

The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allow efficient automated mapping. Engineering structures like bridges shadow global navigation satellite system (GNSS), which complicates precise localization. Simultaneous localization and mapping (SLAM) algorithms offer a sufficient solution, since they do not require GNSS. However, testing and comparing SLAM algorithms in GNSS-denied areas is difficult due to missing ground truth data. This work presents an approach to measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner Leica RTC360 and a tachymeter to acquire point cloud and trajectory information. The proposed method is independent of time synchronization between robot and tachymeter and also works on sparse SLAM point clouds. For the evaluation of the proposed method, three LiDAR-based SLAM algorithms called KISS-ICP, SC-LIO-SAM, and MA-LIO are tested using a UGV equipped with two light detection and ranging (LiDAR) sensors and an inertial measurement unit (IMU). KISS-ICP is based solely on a single LiDAR scanner and SC-LIO-SAM also uses an IMU. MA-LIO, which allows multiple (different) LiDAR sensors, is tested on a horizontal and vertical one and an IMU. Time synchronization between the tachymeter and SLAM data during post-processing allows calculating the root mean square (RMS) absolute trajectory error, mean relative trajectory error, and the mean point cloud to reference point cloud distance. It shows that the proposed method is an efficient approach to measure the performance of SLAM in GNSS-denied areas. Additionally, the method shows the superior performance of MA-LIO in four of six test tracks with 5 to 7 cm RMS trajectory error, followed by SC-LIO-SAM and KISS-ICP in last place. SC-LIO-SAM reaches the lowest point cloud to reference point cloud distance in four of six test tracks, with 4 to 12 cm.

Real-Time Adaptive Data Transmission Against Various Traffic Load in Multi-LIDAR Sensor Network for Indoor Monitoring

One of the key components in smart cities is smart monitoring, a process for collecting information to efficiently manage the infrastructures and resources and enables citizens to engage in intelligent activities. For both indoor and outdoor monitoring, light detection and ranging (LIDAR) technology has been used to collect three-dimensional data useful for smart monitoring. Blind spots can be effectively covered by aggregating point clouds from multiple LIDAR sensors (sensors hereafter) with different viewpoints. Although multiple-LIDAR (multi-LIDAR) sensor networks have been investigated, prior studies have focused on monitoring outdoor scenes with a relatively low number of sensors, mostly for road safety. For indoor use, however, ten or more sensors are required to eliminate blind spots, as the placement of sensors is limited by ceilings. When the network bandwidth is limited because of other traffic loads, the transmission size needs to be small while including the highly important points. Therefore, adaptive control is required for sensor networks. We propose a system that adaptively controls the data volume of a multi-LIDAR sensor network considering the current network state in real time. It consists of a single edge computer and multiple sensors. While aggregating data streams, the edge computer monitors the current delay status for each sensor and sends notifications to change the data volume by the sensor.We report on the results of two experimental evaluations conducted in different environments: one to validate the effectiveness of the proposed system and the other to verify its practicality.

Asynchronous Multiple LiDAR-Inertial Odometry Using Point-Wise Inter-LiDAR Uncertainty Propagation

In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common practice is to transform points among sensors while requiring strict time synchronization or approximating transformation among sensor frames. Unlike existing methods, we elaborate the inter-sensor transformation using continuous-time (CT) inertial measurement unit (IMU) modeling and derive associated ambiguity as a point-wise uncertainty. This uncertainty, modeled by combining the state covariance with the acquisition time and point range, allows us to alleviate the strict time synchronization and to overcome FOV difference. The proposed method has been validated on both public and our datasets and is compatible with various LiDAR manufacturers and scanning patterns. We open-source the code for public access at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/minwoo0611/MA-LIO</uri>

Efficient Detection and Tracking of Human Using 3D LiDAR Sensor

Light Detection and Ranging (LiDAR) technology is now becoming the main tool in many applications such as autonomous driving and human–robot collaboration. Point-cloud-based 3D object detection is becoming popular and widely accepted in the industry and everyday life due to its effectiveness for cameras in challenging environments. In this paper, we present a modular approach to detect, track and classify persons using a 3D LiDAR sensor. It combines multiple principles: a robust implementation for object segmentation, a classifier with local geometric descriptors, and a tracking solution. Moreover, we achieve a real-time solution in a low-performance machine by reducing the number of points to be processed by obtaining and predicting regions of interest via movement detection and motion prediction without any previous knowledge of the environment. Furthermore, our prototype is able to successfully detect and track persons consistently even in challenging cases due to limitations on the sensor field of view or extreme pose changes such as crouching, jumping, and stretching. Lastly, the proposed solution is tested and evaluated in multiple real 3D LiDAR sensor recordings taken in an indoor environment. The results show great potential, with particularly high confidence in positive classifications of the human body as compared to state-of-the-art approaches.

Combining acoustic tracking and LiDAR to study bat flight behaviour in three-dimensional space

BackgroundHabitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ.MethodsHere, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats’ positions and vegetation structures.ResultsOur results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light.ConclusionBy combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction.

Estimation of Spatial Features in 3-D-Sensor Network Using Multiple LiDARs for Indoor Monitoring

In recent years, smart city has been attracting attention as a sustainable urban development. Smart monitoring is one of the key components of a smart city, and Light-Detection-and-Ranging (LiDAR) sensor is used as one of the sensors for smart monitoring. A 3D sensor network using multiple LiDAR sensors can be constructed to eliminate blind spots. Prior work presented a system that prioritizes transmission in important regions to prevent data loss in the case of bandwidth limitation. However, prior work has only presented transmission methods and has not shown how to estimate important regions in the point cloud space. This paper proposes a system to estimate important regions using spatial features created based on multiple spatial metrics. The important regions vary from task to task, and in particular, for the task of detecting moving objects, the important regions are the spaces where the moving objects may be located. The accuracy of estimation can be improved by creating spatial features based on more spatial metrics. This paper addresses two kinds of spatial metrics, temporal metric and statistical metric. In this work, we collect point-cloud data using multiple LiDAR sensors and produce datasets with labels for evaluation. Using datasets, we validate the proposed system in terms of the accuracy of spatial feature estimation.

Estimation of ship berthing parameters based on Multi-LiDAR and MMW radar data fusion

Achieving autonomous and safe berthing can reflect the intelligence level of unmanned ships, which is also an important manifestation to ensure the safe navigation of ships. As high-precision sensors, LiDAR (Light Detection and Ranging) and MMW Radar (millimeter wave radar) can provide environmental data for berthing. However, the use of one of them has certain shortcomings, and the fusion of the data from both sensors can provide more accurate and ample berthing information. Therefore, a novel data fusing method is proposed for assisting berthing. This method combines multi-solid-state LiDAR point cloud and MMW Radar data to obtain berthing parameters. Firstly, the collected data are pre-processed according to the characteristics of asynchronous sensors separately, and then the berthing parameters under the two sensors are obtained by the relative spatial position between the ship and the berth; In addition, the data-level fusion of multiple LiDARs point cloud is achieved by the point cloud registration-based 3D-NDT (Normal Distributions Transform) algorithm, and the decision-level fusion of berthing parameters acquired from the multi-LiDAR point cloud and MMW Radar data is performed by the weighted data fusion algorithm. Two sensors were used as perceptual devices to effectively monitor the berthing process of the experimental Ro-Ro ship at Pikou Port. The results demonstrate that the proposed method can make the data of the two sensors complement each other and effectively estimate model-free berthing parameters, which is important to assist in achieving efficient and safe berthing of ships.

Extrinsic Calibration of Multiple 3D LiDAR Sensors by the Use of Planar Objects.

Three-dimensional light detection and ranging (LiDAR) sensors have received much attention in the field of autonomous navigation owing to their accurate, robust, and rich geometric information. Autonomous vehicles are typically equipped with multiple 3D LiDARs because there are many commercially available low-cost 3D LiDARs. Extrinsic calibration of multiple LiDAR sensors is essential in order to obtain consistent geometric information. This paper presents a systematic procedure for the extrinsic calibration of multiple 3D LiDAR sensors using plane objects. At least three independent planes are required within the common field of view of the LiDAR sensors. The planes satisfying the condition can easily be found on objects such as the ground, walls, or columns in indoor and outdoor environments. Therefore, the proposed method does not require environmental modifications such as using artificial calibration objects. Multiple LiDARs typically have different viewpoints to reduce blind spots. This situation increases the difficulty of the extrinsic calibration using conventional registration algorithms. We suggest a plane registration method for cases in which correspondences are not known. The entire calibration process can easily be automated using the proposed registration technique. The presented experimental results clearly show that the proposed method generates more accurate extrinsic parameters than conventional point cloud registration methods.

Comparing tree structures derived among airborne, terrestrial and mobile LiDAR systems in urban parks

ABSTRACT Measuring tree structure using three-dimensional (3D) mapping tools such as light detection and ranging (LiDAR) remote sensing is needed to provide well-managed and designed green spaces. The metrics used to estimate tree structure could be different depending on which LiDAR systems are used. This may lead to confusion and reduce confidence when evaluating tree structures and their derived products, such as plant area index (PAI). Therefore, studies that can determine similarities among measurements derived from different LiDAR systems are needed. In this study, structural canopy metrics in airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning (MLS) were compared to seek consistencies among the three LiDAR systems. The specific objectives were to test whether the estimates made by the metrics differed depending on single or clustered trees and to test whether LiDAR-derived errors in the metrics are related to tree structures. Tree point clouds were manually classified into single and clustered trees. Heights-related metrics, Rumple Index, area, and PAI were calculated for comparison analysis. Root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (r) were calculated to evaluate the consistencies in each metric among the LiDAR systems. The results showed that the maximum height of the point clouds (ZMAX) and max and mean heights derived from the canopy height models (minCHM and maxCHM) demonstrated good consistency (RMSE% < 10%, Bias% < 10%, and r > 0.900). Moreover, the biases from the ZMAX- and CHM-derived metrics comparisons among the LiDAR systems did not show strong linear relationships with the tree heights and canopy complexities (i.e. Pearson’s correlation coefficient r < |0.29|). On the contrary, the 95th percentile (Zq95) height and mean z height (ZMEAN) differed depending on the tree classes and showed significant linear relations with canopy heights and complexity. The configurations of LiDAR systems, such as point density and sensing locations, seem to affect the Zq95, ZMEAN metrics, and PAI. Our results suggest that assessing for consistencies among the different LiDAR systems is required before using multiple LiDAR systems interchangeably to estimate the structure of urban park areas.

Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments

The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.

A Tightly-Coupled Framework for Large-Scale Map Construction With Multiple Non-Repetitive Scanning LiDARs

With the ability of providing direct and accurate enough range measurements, light detection and ranging (LiDAR) is playing an essential role in localization and detection for autonomous vehicles. Since single LiDAR suffers from hardware failure and performance degradation intermittently, we present a multi-LiDAR integration scheme in this article. Our framework tightly couples multiple non-repetitive scanning LiDARs with inertial, encoder, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. Primarily, we formulate a precise synchronization strategy to integrate isolated sensors, and the extracted feature points from separate LiDARs are merged into a single sweep. The fused scans are introduced to compute the scan-matching correspondences, which can be further refined by additional real-time kinematic (RTK) measurements. Based thereupon, we construct a factor graph along with the inertial preintegration result, estimated ground constraints, and RTK data. For the purpose of maintaining a restricted number of poses for estimation, we deploy a keyframe based sliding-window optimization strategy in our system. The real-time performance is guaranteed with multi-threaded computation, and extensive experiments are conducted in challenging scenarios. Experimental results show that the utilization of multiple LiDARs boosts the system performance in both robustness and accuracy.

Projection and integration of connected-infrastructure LiDAR sensing data in a global coordinate

Recently, roadside Light Detection and Ranging (LiDAR) has been deployed for different transportation applications such as high-resolution-micro-traffic data collection, vehicle–pedestrian safety evaluation, and driver’s behavior analysis. An ideal LiDAR-enhanced traffic infrastructure system needs multiple LiDAR sensors deployed around intersections and along road segments, which generate a seamless coverage of intersections or arterials. To obtain continuous and complete traffic data, the integration method is much more important to extend the data range and improve the density of scanned points. In this research, an innovative approach based on the GPS mapping method was present to automatically integrate data collected by different LiDAR sensors in a global coordinate. In this method, the raw data collected by multiple LiDAR sensors are used as input, at least 4 reference points collected by GPS devices are needed for each LiDAR sensor, then a transformation step is applied to transform all the LiDAR points into the Earth-centered, Earth-fixed coordinate system. After obtaining all the LiDAR points in the global coordinate system, an Iterative Closest Point (ICP) method was used to reduce the errors caused by data collection and calculation. The sensitivity analysis part provided the best number for the reference points collection. At last, the data collected at two sites (Evans & McCarran intersection and Blue parking lot of University of Nevada, Reno (UNR) were selected to verify the method. The testing results showed that the proposed method has a high level of automation and improved accuracy.

Extrinsic Calibration of Dual LiDARs Based on Plane Features and Uncertainty Analysis

Solutions equipped with two or more light detection and ranging (LiDAR) systems have been applied in several fields (navigation, mapping, target detection, and others) to obtain increasingly detailed 3D environmental information. Accurate calibration of multiple LiDAR systems is necessary for integrating and using their data. This study improves a convenient and reliable method for calibrating the relative poses between 3D LiDARs. The planar surfaces of a scene are measured from different poses; then, the planes are extracted and constraints are constructed to solve extrinsic parameters. The factors influencing calibration uncertainty are also investigated. It is found that the uncertainty is closely related to the geometric structure of the plane observations, and the rotation dilution of precision and translation dilution of precision are defined to denote this effect. The influence of different types of spatial plane geometries on calibration errors is discussed, and optimal observation conditions are derived. Experimental results for simulation and real data show that the proposed method can achieve an accurate extrinsic calibration of LiDARs, and the point cloud registration errors after calibration are less than 2 cm. The analysis of the calibration uncertainty provides a valuable reference and guide for experimental design and error evaluation.

Automatic Multiple LiDAR Calibration Based on the Plane Features of Structured Environments

The multiple light detection and ranging (LiDAR) system has been attached to numerous autonomous vehicles to reduce blind spot risks and increase measurement resolution. It is also important to accurately aligning multi-LiDAR data because misaligned sensor data can affect the localization and perception algorithms of LiDAR sensors. However, precise extrinsic parameter estimation is a challenging research area because the feature points in LiDAR data cannot be precisely aligned owing to the LiDAR resolution. In this paper, we proposed a targetless multi-LiDAR calibration method without any initial information. In the proposed approach, a random sample consensus (RANSAC)-based plane fitting technique was utilized for plane extraction, and cross-feature points were extracted from the polar view transform of segmented planes. To resolve the discontinuity problem of optimization, a nonlinear optimization method was utilized, and the experiments were performed within a structured environment. Overall, the experimental results showed that the proposed method yields comparable results with state-of-the-art methods.

Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.

Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices

Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people's mobility in smart monitoring. Due to the sheer volume of point cloud data, data transmission requires a significant amount of communication resources. In order to monitor people's mobility in real time, it is necessary to reduce the amount of transmission data to shorten delay. Point cloud compression is one method for reducing the amount of data. However, prior works addressing point cloud compression mainly focused on accuracy for the compression of an entire point cloud without considering its spatial characteristics. The more dynamically a spatial region changes, the more important it is when detecting moving objects such as cars, trucks, pedestrians, and bikes in smart monitoring. This paper proposes a prioritized transmission scheme that applies multiple point cloud compression methods to point cloud data according to the spatial importance of the data, i.e., how dynamically spatial regions change. This paper assumes data transmission of point cloud data from multiple LIDAR devices to an edge server and addresses the intra-frame geometry compression of point cloud data. The proposed scheme splits the point cloud into multiple classes according to the spatial importance and applies multiple point cloud compression methods to each class. A numerical study using a real point cloud dataset obtained at an intersection demonstrates the dependencies of quality, volume, and processing time on possible compression format options. The results verify that the proposed scheme reduces the amount of point cloud data drastically while satisfying the quality and processing time requirements.

Identifying and mapping potentially adverse discontinuities in underground excavations using thermal and multispectral UAV imagery

Geotechnical mapping, rock mass characterization, and inspections of underground excavations are critical to ensuring that excavation geometry and ground support design are appropriate for stable conditions. A lack of access, poor timing, or hazardous conditions are all factors that can prohibit engineering personnel from collecting high-quality data in the field. Advances in unmanned aerial vehicles (UAV) and portable sensor technology now allow engineering personnel to remotely capture data from underground workings that can be used for developing designs, generating inputs for analyses, and making more informed decisions. Ground falls are a source of many accidents in the underground environment, and they are the focus of many geotechnical investigations and analyses. In this study, the authors investigated how portable UAV-mounted thermal and multispectral imagers could be used to detect and quantify adverse geological discontinuities in hard rock masses that are structurally controlled. Multiple thermal, multispectral, RGB (red, green, and blue), and LiDAR (light detection and ranging) data sets were captured from 5 flights in sub-level open stopes at the Barrick Golden Sunlight Mine in the state of Montana in the western United States of America. Using off-the-shelf software, the thermal, RGB, and multispectral images were processed to create individual three-dimensional point clouds and meshes, which were georeferenced using the LiDAR data. Discontinuities identified in the three-dimensional point clouds and meshes were mapped using tools found in the freeware CloudCompare. One of the thermal models identified wedges that had developed in the crown pillar of an open stope, and 4 of the multispectral models contained enough detail for mapping individual discontinuities. The results of this investigation indicate that these portable imagers are viable tools that can be used to aid engineering personnel in identifying and mapping adverse geological discontinuities and unique rock mass composition. The techniques for capturing, processing, and interpreting thermal and multispectral imagery captured in underground excavations are described in this paper and can be used as the basis for future investigations at individual underground sites.

Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns

LiDAR (Light Detection and Ranging) technology has been increasingly implemented to assess the biophysical attributes of forest canopies. However, LiDAR-based estimation of tree biophysical attributes remains difficult mainly due to the occlusion of vegetative elements in multi-layered tree crowns. In this study, we developed a new algorithm along with a multiple-scan methodology to analyse the impact of occlusion on LiDAR-based estimates of tree leaf area. We reconstructed five virtual tree models using a computer graphic-based approach based on in situ measurements from multiple tree crowns, for which the position, size, orientation and area of all leaves were measured. Multi-platform LiDAR simulations were performed on these 3D tree models through a point-line intersection algorithm. An approach based on the Delaunay triangulation algorithm with automatic adaptive threshold selection was proposed to construct the scanned leaf surface from the simulated discrete LiDAR point clouds. In addition, the leaf area covered by laser beams in each layer was assessed in combination with the ratio and number of the scanned points. Quantitative comparisons of LiDAR scanning for the occlusion effects among various scanning approaches, including fixed-position scanning, multiple terrestrial LiDAR scanning and airborne-terrestrial LiDAR cross-scanning, were assessed on different target trees. The results showed that one simulated terrestrial LiDAR scan alongside the model tree captured only 25–38% of the leaf area of the tree crown. When scanned data were acquired from three simulated terrestrial LiDAR scans around one tree, the accuracy of the leaf area recovery rate reached 60–73% depending on the leaf area index, tree crown volume and leaf area density. When a supplementary airborne LiDAR scanning was included, occlusion was reduced and the leaf area recovery rate increased to 72–90%. Our study provides an approach for the measurement of total leaf area in tree crowns from simulated multi-platform LiDAR data and enables a quantitative assessment of occlusion metrics for various tree crown attributes under different scanning strategies.