Derived Point Clouds Research Articles

Enhanced trajectory estimation of mobile laser scanners using aerial images

Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with accurate exterior orientations instead of using ground control points. The MLS trajectory is estimated based on observation equations resulting from these corresponding points, the original IMU observations, and soft constraints on the pitch and yaw rotations of the vehicle. We analyse the quality of the trajectory enhancement under several conditions where the experiments were designed to test the influence of the number and quality of corresponding points and to test different settings for a B-spline representation of the vehicle trajectory. The method was tested on two independently acquired MLS datasets in Rotterdam by enhancing the trajectories and evaluating them using checkpoints. The RMSE values of the original GNSS/IMU based Kalman filter results at the checkpoints were 0.26 m, 0.30 m, and 0.47 m for the X-, Y- and Z-coordinates in the first dataset and 1.10 m, 1.51 m, and 1.81 m in the second dataset. The latter dataset was recorded with a lower quality IMU in an area with taller buildings. After trajectory adjustment these RMSE values were reduced to 0.09 m, 0.11 m, and 0.16 m for the first dataset and 0.12 m, 0.14 m, and 0.18 m for the second dataset. The results confirmed that, if sufficient tie points between the point cloud and aerial imagery are available, the method supports geo-referencing of MLS point clouds in urban canyons with a near-decimetre accuracy.

An Automatic Tree Skeleton Extraction Approach Based on Multi-View Slicing Using Terrestrial LiDAR Scans Data

Effective 3D tree reconstruction based on point clouds from terrestrial Light Detection and Ranging (LiDAR) scans (TLS) has been widely recognized as a critical technology in forestry and ecology modeling. The major advantages of using TLS lie in its rapidly and automatically capturing tree information at millimeter level, providing massive high-density data. In addition, TLS 3D tree reconstruction allows for occlusions and complex structures from the derived point cloud of trees to be obtained. In this paper, an automatic tree skeleton extraction approach based on multi-view slicing is proposed to improve the TLS 3D tree reconstruction, which borrowed the idea from the medical imaging technology of X-ray computed tomography. Firstly, we extracted the precise trunk center and then cut the point cloud of the tree into slices. Next, the skeleton from each slice was generated using the kernel mean shift and principal component analysis algorithms. Accordingly, these isolated skeletons were smoothed and morphologically synthetized. Finally, the validation in point clouds of two trees acquired from multi-view TLS further demonstrated the potential of the proposed framework in efficiently dealing with TLS point cloud data.

Using sUAS-Derived Point Cloud to Supplement LiDAR Returns for Improved Canopy Height Model on Earthen Dams

Of the nearly 2,400 regulated dams in South Carolina, USA, 97% are earthen dams. Many of these dams are covered in dense vegetation that impede inspections and increase slope instability. Because of their relatively spatially coarse collections, LiDAR datasets are often insufficient on small earthen dams for adequately characterizing both the ground surface and vegetative cover. This study tests the feasibility of small Unmanned Aerial Systems (sUAS) derived “image returns” in extracting a canopy height model (CHM) on a private dam in Columbia, South Carolina. Following traditional methods for creating a CHM from 3D point clouds, both LiDAR- and sUAS image-derived datasets were used to create a composite CHM. A validation process used a set of 30 in situ measured tree heights compared to their modeled heights from the sUAS only, LiDAR only, and composite CHMs. In terms of the root mean squared error (RMSE), the LiDAR only model (5.60 m) outperformed the sUAS only (8.17) and LiDAR + sUAS (7.23 m) composite models. Findings from this study suggest that sUAS derived point clouds have potential, but the structure from motion (SfM) technology and supporting ground control need to be improved before operational application on canopy height modeling for dense vegetation over earthen dams.

Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests

Tree species composition of forest stand is an important indicator of forest inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying forest biodiversity. In this study, we acquired high spatial resolution multispectral and RGB imagery over a subtropical natural forest in southwest China using a fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral and RGB derived point clouds, upon which individual tree crown (ITC) delineation algorithms and a machine learning classifier were used to identify dominant tree species. To do so, the structure-from-motion method was used to generate RGB imagery-based DAP point clouds. Then, three ITC delineation algorithms (i.e., point cloud segmentation (PCS), image-based multiresolution segmentation (IMRS), and advanced multiresolution segmentation (AMRS)) were used and assessed for ITC detection. Finally, tree-level metrics (i.e., multispectral, texture and point cloud metrics) were used as metrics in the random forest classifier used to classify eight dominant tree species. Results indicated that the accuracy of the AMRS ITC segmentation was highest (F1-score = 82.5 %), followed by the segmentation using PCS (F1-score = 79.6 %), the IMRS exhibited the lowest accuracy (F1-score = 78.6 %); forest types classification (coniferous and deciduous) had a higher accuracy than the classification of all eight tree species, and the combination of spectral, texture and structural metrics had the highest classification accuracy (overall accuracy = 80.20 %). In the classification of both eight tree species and two forest types, the classification accuracies were lowest when only using spectral metrics, indicated that the texture metrics and point cloud structural metrics had a positive impact on the classification (the overall accuracy and kappa accuracy increased by 1.49–4.46 % and 2.86–6.84 %, respectively).

Structure‐from‐motion photogrammetry analysis of historical aerial photography: Determining beach volumetric change over decadal scales

AbstractHistorical aerial photographs are an invaluable tool in shoreline mapping and change detection in coastal landscapes. We evaluate the extent to which structure‐from‐motion (SfM) photogrammetric methods can be applied to quantify volumetric changes along sandy beaches, using archival imagery. We demonstrate the application of SfM‐derived digital surface models (DSMs) at East Beach and Lady Bay in southwest Victoria, Australia, using photographic datasets taken in 1969, 1977 and 1986, and compare them to LiDAR‐derived DSMs acquired at both sites in 2007. The SfM approaches resulted in two entire and two partial suitable DSMs out of six datasets. Good‐quality DSMs were spatially continuous with a good spread of ground control points (GCPs) near the beach at Lady Bay, whereas unsuitable DSMs were mostly restricted by poor distribution and number of GCPs in spatially segmented areas of East Beach, due to limited overlapping of images, possible poor quality of GCPs and also the propagation of errors in the derived point clouds. A volume of approximately 223 000 ± 72 000 m3 was deposited at Lady Bay between 1969 and 2007, despite minimal erosion observed near the breakwater. The partially suitable dataset of East Beach indicated that beach erosion of at least 39 m3 m−1 occurred immediately to the east of the seawall after 1977. We also discuss the drawbacks and strengths of SfM approaches as a benchmark of historical erosion assessments along sandy beaches. © 2020 John Wiley & Sons, Ltd.

Temporal multispectral and 3D analysis of Cerro de Pasco, Peru

Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m3. Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m3 between 2001 and 2018. These presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.

Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

UAV survey at archaeological earthwork sites in the Brazilian state of Acre, southwestern Amazonia

AbstractUnmanned aerial vehicles (UAVs) are increasingly used for many scientific applications, including archaeological surveys. We test the suitability and practicability of UAV surveying in the tropical lowlands of Brazil and techniques for visualizing the resulting digital elevation models, specifically the Red Relief Image Map (RRIM). We present the results of UAV surveys conducted at four diverse archaeological earthwork sites situated in interfluvial southwestern Amazonia, in the state of Acre. The elevation models produced from UAV derived point clouds display clear patterns in the site layouts and reveal subtle intra‐site earthwork features that are not easily discernible on the ground. Our study demonstrates that UAVs are cost efficient and give highly detailed results for topographic mapping and visualization of archaeological features when vegetation cover is sufficiently low and sparse. The rapid data capture and lack of spatial sampling bias of the UAV data collection is a significant advantage compared to conventional mapping methods. Furthermore, UAV surveying and UAV derived data processing do not require expensive technologies or specialized user expertise, since open‐source software and easy‐to‐use toolkits are readily available.

Describing seasonal differences in tree crown delineation using multispectral UAS data and structure from motion

ABSTRACTUnmanned Aerial Systems (UAS) have emerged as a platform capable of providing valuable data on vegetation structure, health, and productivity. The platform provides maximum temporal flexibility in data collection and customizability of spatial footprints which is important for mapping and monitoring vegetation conditions especially for high spectrally and structurally heterogeneous landscapes. In this letter, we investigate the potential improvement in structural information when using sensor payloads that include infrared bands (730–810 nm) in addition to the typical visible bands of many UAS sensor payloads. Specifically, we use derived point clouds and imagery collected from UAS-mounted sensors at a test site in Louisville, Kentucky, USA to assess whether multispectral sensors improve estimates of vegetation structure for plant greenness (leaf-on) and senescence (leaf-off) periods. Results indicate that including detailed multispectral reflectance information beyond the visible portion of the electromagnetic spectrum aids in estimating structural characteristics of woody vegetation, regardless of season. While both leaf-on and leaf-off periods result in reasonable crown height estimates, leaf-on conditions also improve radial estimates, likely due to continuous canopy surfaces. These findings advance research for remote sensing analyses assessing structural composition in heterogeneous landscapes where varying levels of vegetation structure have implications on land use and land function.

LOW-COST WHEELED ROBOT-BORNE LASER SCANNING SYSTEM FOR INDOOR AND OUTDOOR 3D MAPPING APPLICATION

Abstract. Aiming to accomplish automatic and real-time three-dimensional mapping in both indoor and outdoor scenes, a low-cost wheeled robot-borne laser scanning system is proposed in this paper. The system includes a laser scanner, an inertial measurement unit, a modified turtlebot3 two-wheel differential chassis and etc. To achieve a globally consistent map, the system performs global trajectory optimization after detecting the loop closure. Experiments are undertaken in two typical indoor/outdoor scenes that is an underground car park and a road environment in the campus of Wuhan University. The point clouds acquired by the proposed system are quantitatively evaluated by comparing the derived point clouds with the ground truth data collected by a RIEGL VZ 400 laser scanner. The results present an accuracy of 90% points below 0.1 meter error in the tested scene, showing that its applicability and potential in indoor and mapping applications.

GEOMETRIC OBJECT BASED BUILDING RECONSTRUCTION FROM SATELLITE IMAGERY DERIVED POINT CLOUDS

Abstract. 3D building models are needed for urban planning and smart city. These models can be generated from stereo aerial images, satellite images or LiDAR point clouds. In this paper, we propose a geometric object-based building reconstruction method from satellite imagery derived point clouds. The goal is to achieve a geometrically correct, topologically consistent, and non-redundant 3D representation for buildings in urban areas. The paper first introduces our motivation, followed by a comprehensive review on related works. We then introduce the methodology and process developed in this paper. Primary results from the point clouds generated from WorldView high resolution satellite images are used to demonstrate the performance of the approach.

Modeling the Stereoscopic Features of Mountainous Forest Landscapes for the Extraction of Forest Heights from Stereo Imagery

Spaceborne stereoscopic systems have been growing in recent years, and the point cloud extracted from spaceborne stereo imagery has been used to measure forest spatial structures. These systems work on different viewing angles and image spatial resolutions, which are two critical factors determining the quality of the derived point cloud. In addition, the complex terrain is also a great challenge for the regional mapping of forest spatial structures using spaceborne stereo imagery. Although several theoretical models for simulating multi-view spectral features of forest canopies have been developed, there is hardly any report of a stereoscopic analysis using these models due to the limited size of the simulated forest scenes and the lack of a geometric sensory model (i.e., physical relationship between two-dimensional image coordinates and three-dimensional georeferenced coordinates). The stereoscopic features (i.e., parallax) are, as important as the spectral features contained in the multi-view images of a targeted area, the basis for the extraction of a point cloud. In this study, a new model, referred to as LandStereo model, has been proposed, which is capable of simulating the stereoscopic features of forest canopies over mountainous areas at landscape scales. The model comprised five parts, including defining the mountainous forest landscapes, setting the sun-senor observation geometry, simulating images, generating ground control points, and building geometric sensor models. The LandStereo model was validated over three different scenes, including flat forest landscapes, bare mountain landscapes, and mountainous forest landscapes. The results clearly demonstrated that the LandStereo model worked well on simulating stereoscopic features of both terrains and forest canopies at landscape scales. The extracted height of a forest canopy top from simulated stereo imagery was highly correlated to the truth (R2 = 0.96 and RMSE = 0.99 m) over the flat terrains and (R2 = 0.92 and RMSE = 1.15 m) over the mountainous areas. The LandStereo model provided a powerful tool to further our understanding of the relationships between forest spatial structures and point cloud extracted from stereo imagery acquired from different view angles and spatial resolutions under complex terrain conditions.

Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation

ABSTRACTThe vast size of oil palm (Elaeis guineensis) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and >75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (R2 = 0.63) for 7 year old and 0.45 m (R2 = 0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (>75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.

Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success

Mining is an important human activity that significantly affects the landscape character, particularly through excavation of spoil material and its deposition on spoil banks. The information on terrain or vegetation cover of spoil banks is often required for two different reasons: (i) to monitor and prevent adverse effect of hazards associated with unstable terrain; and (ii) to assess restoration success. Traditionally used in situ methods for monitoring surface displacement or restoration success are restricted in terms of spatial and temporal coverage. Therefore, in this study, we assessed the value of photogrammetrically and Light Detection and Ranging (LiDAR) derived point clouds for characterizing a post-mining site. We acquired images under leaf-off and leaf-on conditions and showed that point densities of point clouds acquired photogrammetrically under leaf-off conditions exceeded densities of those acquired under leaf-on conditions and uniformly covered ground of the entire study area (an average density of 288 points per m2). In addition, the accuracy of the digital terrain model (DTM; 1 m resolution) derived from images acquired under leaf-off conditions was comparable to the LiDAR-derived DTM (RMSE of 0.19 m and 0.12 m, respectively). While LiDAR-derived DTM accuracies were consistent across vegetation categories (RMSE 0.12–0.14 m), accuracy of image-based DTMs declined in the following order: forest (RMSE 0.15 m), steppes (RMSE 0.21 m), and aquatic vegetation (RMSE 0.36 m). We suggest the leaf-off UAV imagery as a viable alternative for building DTMs that can be utilized for assessment of risks associated with instability of spoil banks terrain. In addition, we also suggest that a combination of acquisitions under leaf-off and leaf-on conditions have a potential to replace expensive airborne LiDAR surveys for applications requiring information on vegetation cover or vegetation height.

Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.

Rockfall Simulation Based on UAV Photogrammetry Data Obtained during an Emergency Declaration: Application at a Cultural Heritage Site

In recent years, there was an increasing number of studies focusing on rockfalls due to their impacts on social and sustainable development. This work carries out a three-dimensional (3D) simulation of rockfalls at a cultural heritage site nearby the village of Cortes de Pallás (Valencian Community, East Spain). The simulation is based on data collected previously, during an emergency declaration due to the occurrence of a considerable rockfall (7980 m3) on the southern bank of the Cortes de Pallás reservoir, on 6 April 2015. The hydroelectric power plant was damaged, and the main access road to the village of Cortes de Pallás was blocked for eight months. The predominant discontinuities of the rock mass were analyzed by means of the application of structure from motion (SfM) photogrammetry techniques to the set of images taken by remotely piloted aircraft systems (RPAS). The average size of the block was determined as 3.2 m in diameter and 17.6 m3 in volume. Additionally, a digital elevation model (DEM) was generated from an aerial laser scanning (ALS)-derived point cloud using a 1 × 1 grid. These data were implemented in RocPro3D software, obtaining the distances traveled by the blocks detached from different source areas at a cultural heritage site located near the rockfall event, which presents the same geological context. The simulation presented herein shows aggravating circumstances that endanger the cultural heritage area, with higher rockfall hazards than previous official studies (1991) displayed.

Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions

ABSTRACTUnmanned aerial vehicle (UAV) platforms are rapidly becoming popular in many research and industry sectors. Due to their relatively low purchase price and the fact they can be used to monitor areas that are difficult or even unsafe to access, they have been increasingly used in land surveying and mapping of smaller areas. Numerous UAV platforms equipped with various cameras are increasingly available on the market, differing in their suitability for environmental mapping. Surveyors therefore face a question whether to buy or assemble their own UAV. The objective of this study is to assess the performance of two fixed-wing UAV systems for land survey and mapping applications. In particular, we: (1) compared a commercial eBee platform equipped with a Sony Cybershot DSC-WX220 camera with zoom lens and a home assembled EasyStar II equipped with Nikon Coolpix A with a lens of fixed focal length to find out if a home-assembled solution can compete with specialized commercial platform; (2) investigated the utilization of UAV images acquired under leaf-off conditions for digital terrain model (DTM) generation with respect to vegetation cover (steppes and forests); (3) assessed whether an increase in the image quantity can compensate for a lower quality of images; and (4) compared the DTM derived from UAV imagery with the official Czech Republic airborne laser scanning (ALS)-derived DTM. One flight with Easystar II and two perpendicular flights with eBee were performed. From these three flights, four point clouds were derived (one from each flight, and one resulting from a combination of two eBee flights), supplemented with four ground filtered point clouds. The accuracy of point clouds and DTM was assessed through a comparison with a conventional GNSS survey. We successfully identified the bare ground during the leaf-off period in the deciduous forest using images from both platforms. Point densities of point clouds acquired with Easystar II exceeded the densities of those acquired with eBee even after combining images from two eBee flights. Root mean square error of all derived point clouds ranged between 0.11 and 0.19 m, exceeding the accuracy of a nationwide ALS-derived DTM in both forest and open steppe areas. The most accurate point cloud was acquired using Easystar II. This is likely due to a combined effect of the quality of onboard cameras, camera settings and environmental conditions during the flight. For users who prefer to have greater control over their options rather than being dependent on the commercially available kit solution, home-assembled kits utilizing drones capable of carrying any camera available on the market may be an advantage.

Bias Impact Analysis and Calibration of Terrestrial Mobile LiDAR System With Several Spinning Multibeam Laser Scanners

This paper proposes a multiunit light detection and ranging (LiDAR) system calibration procedure to directly estimate the mounting parameters relating multiple spinning multibeam laser scanners to the global navigation satellite system/inertial navigation system (GNSS/INS) unit onboard a mobile terrestrial platform in order to derive point clouds with high-positional accuracy. This procedure is based on the use of conjugate planar/linear features in overlapping point clouds derived from different drive runs. In order to increase the efficiency of semiautomatic conjugate feature extraction from LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within outdoor environments are used in this paper. To ensure the accuracy of the estimated mounting parameters, an optimal configuration of target primitives and drive runs is determined by analyzing the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud for different orientations of target primitives and different drive run scenarios. This impact is also verified experimentally by simulating a bias in each mounting parameter separately. Finally, the optimal configuration is used within an experimental setup to evaluate the performance of the proposed calibration procedure through the a posteriori variance factor of least squares adjustment and the quality of fit of adjusted point cloud to linear/planar surfaces before and after the calibration process. The proposed calibration approach attained an accuracy of 1.42 cm, which is better than the accuracy expected based on the specifications of the involved hardware components, i.e., the LiDAR and GNSS/INS units.

STOCHASTIC SURFACE MESH RECONSTRUCTION

Abstract. A generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.

3D mapping of discontinuity traces using fusion of point cloud and image data

A new methodology is presented for 3-D automated mapping of joints that are exposed primarily as traces in a rock face as opposed to planar facets. The method identifies 3-D points in a photogrammetry or a LiDAR derived point cloud that corresponds to the traces of the joints as observed in image data. First, the 2-D trace texture is extracted from image data using a hybrid global and local threshold method and integrating a series of image-processing algorithms. Second, data matching links the pixel locations corresponding to the identified traces in an image to the 3-D coordinates in the point cloud. This matching is accomplished by a coordinate transformation between the image coordinates and point cloud coordinates. Finally, a 3-D discontinuity trace map is acquired by analysing the 3-D spatial features of the traces. A case study of a rock slope along a highway is presented using the proposed method. The results demonstrate that the fusion of image data and point cloud data improves the mapping of discontinuities that primarily appear as traces in outcrops versus that achieved by existing methods that rely only on point cloud data.