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In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor. [ABSTRACT FROM AUTHOR]

As climate changes, maintenance of yield stability requires efficient selection for drought tolerance. Drought-tolerant cultivars have been successfully but slowly bred by yield-based selection in arid environments. Marker-assisted selection accelerates breeding but is less effective for polygenic traits. Therefore, we investigated a selection based on phenotypic markers derived from automatic phenotyping systems. Our trial comprised 64 potato genotypes previously characterised for drought tolerance in ten trials representing Central European drought stress scenarios. In two trials, an automobile LIDAR system continuously monitored shoot development under optimal (C) and reduced (S) water supply. Six 3D images per day provided time courses of plant height (PH), leaf area (A3D), projected leaf area (A2D) and leaf angle (LA). The evaluation workflow employed logistic regression to estimate initial slope (k), inflection point (Tm) and maximum (Mx) for the growth curves of PH and A2D. Genotype × environment interaction affected all parameters significantly. Tm(A2D)s and Mx(A2D)s correlated significantly positive with drought tolerance, and Mx(PH)s correlated negatively. Drought tolerance was not associated with LAc, but correlated significantly with the LAs during late night and at dawn. Drought-tolerant genotypes had a lower LAs than drought-sensitive genotypes, thus resembling unstressed plants. The decision tree model selected Tm(A2D)s and Mx(PH)c as the most important parameters for tolerance class prediction. The model predicted sensitive genotypes more reliably than tolerant genotype and may thus complement the previously published model based on leaf metabolites/transcripts. [ABSTRACT FROM AUTHOR]

In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.

This study proposes a method for detecting and tracking traversable regions in off-road conditions for unmanned ground vehicles (UGVs). Off-road conditions, such as rough terrain or fields, present significant challenges for UGV navigation, and detecting and tracking traversable regions is essential to ensure safe and efficient operation. Using a 3D laser scanner and range-image-based approach, a method is proposed for detecting traversable regions under off-road conditions; this is followed by a Bayesian fusion algorithm for tracking the traversable regions in consecutive frames. Our range-image-based traversable-region-detection approach enables efficient processing of point cloud data from a 3D laser scanner, allowing the identification of traversable areas that are safe for the unmanned ground vehicle to drive on. The effectiveness of the proposed method was demonstrated using real-world data collected during UGV operations on rough terrain, highlighting its potential as a solution for improving UGV navigation capabilities in challenging environments. [ABSTRACT FROM AUTHOR]

Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed. [ABSTRACT FROM AUTHOR]

Precision viticulture employs various sensors for assessing nondestructively key parameters in vineyards. One of the most promising technologies for this purpose is the laser scanner sensor. Laser scanner uses the LiDAR (Light Detection And Ranging) method for the calculation of the distance from the sensor. However, the number of cultivation operations affects the credibility of sensors such as the laser scanner. The main aim of this study was to assess a laser scanner sensor at different measurement settings for estimating pruning wood parameters on two wine grape cultivars (Sauvignon Blanc and Syrah) that received different numbers of farming interventions. The experiment was conducted in the two vineyards situated in the same farm for two successive years (2014 and 2015). The results indicated that the use of a laser scanner in the Syrah vineyard presented more accurate results (r = 0.966 in 2014 and r = 0.806 in 2015) when compared to the Sauvignon Blanc one (r = 0.839 in 2014 and r = 0.607 in 2015) regarding pruning wood parameters estimation. Different measurement settings and weather conditions had different effects on the accuracy of the sensor. It can be concluded that the laser scanner is a very helpful sensor for estimating pruning wood parameters in vineyards.

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precision viticulture employs various sensors for assessing nondestructively key parameters in vineyards. One of the most promising technologies for this purpose is the laser scanner sensor. Laser scanner uses the LiDAR (Light Detection And Ranging) method for the calculation of the distance from the sensor. However, the number of cultivation operations affects the credibility of sensors such as the laser scanner. The main aim of this study was to assess a laser scanner sensor at different measurement settings for estimating pruning wood parameters on two wine grape cultivars (Sauvignon Blanc and Syrah) that received different numbers of farming interventions. The experiment was conducted in the two vineyards situated in the same farm for two successive years (2014 and 2015). The results indicated that the use of a laser scanner in the Syrah vineyard presented more accurate results (r = 0.966 in 2014 and r = 0.806 in 2015) when compared to the Sauvignon Blanc one (r = 0.839 in 2014 and r = 0.607 in 2015) regarding pruning wood parameters estimation. Different measurement settings and weather conditions had different effects on the accuracy of the sensor. It can be concluded that the laser scanner is a very helpful sensor for estimating pruning wood parameters in vineyards. [ABSTRACT FROM AUTHOR]

This study proposes a method for detecting and tracking traversable regions in off-road conditions for unmanned ground vehicles (UGVs). Off-road conditions, such as rough terrain or fields, present significant challenges for UGV navigation, and detecting and tracking traversable regions is essential to ensure safe and efficient operation. Using a 3D laser scanner and range-image-based approach, a method is proposed for detecting traversable regions under off-road conditions; this is followed by a Bayesian fusion algorithm for tracking the traversable regions in consecutive frames. Our range-image-based traversable-region-detection approach enables efficient processing of point cloud data from a 3D laser scanner, allowing the identification of traversable areas that are safe for the unmanned ground vehicle to drive on. The effectiveness of the proposed method was demonstrated using real-world data collected during UGV operations on rough terrain, highlighting its potential as a solution for improving UGV navigation capabilities in challenging environments.

Mobile light detection and ranging (LiDAR) scanning systems consist of global navigation satellite system (GNSS) and inertial navigation system (INS) equipment for positioning in addition to LiDAR sensors and cameras. All of these units work collaboratively to generate an accurate point cloud positioned in a three-dimensional (3D) geodetic coordinate system. However, various factors affect the measurement quality with mobile LiDAR systems (MLS), and these factors include inertial drift problems, GNSS positioning errors, errors stemming from insufficient calibration, etc. The measurements with multiple scanners in mobile systems require a calibration process to deal with the inconsistency problem in overlapping strips. The calibration is done between the laser, camera, INS, and GNSS sensors. The cameras and laser scanners become ready to use after the calibration stage. The strip adjustment is a crucial process for eliminating the errors, which come from the GNSS/INS measurements and remain despite the calibration process, in obtaining an accurate point cloud of the scanned structure. This study aims testing and clarifying the performance of the iterative closest point (ICP) algorithm in precise registration of LiDAR strips and hence proposes a data-driven approach for strip adjustment in mobile LiDAR scanning applications. In the result of the numerical case study, fine registration of the point clouds obtained from forward and backward scanning using ICP yields a 3.2 cm root mean square error and 1.5 cm mean difference, which corresponds to an almost 84% improvement in mean differences of the point clouds in comparison to the initial phase before the ICP registration. Thus, the results clarify that the ICP algorithm succeeds in the strip adjustment process by reducing the average difference between the mutual points of multi-strip point clouds in mobile LiDAR applications. [ABSTRACT FROM AUTHOR]

With more goods to be transported oversea, traffic and vessels' dimensions increase while berthing areas merely remain constant and thus challenge ship masters and pilots to maneuver in small basins with dense traffic even in bad weather situations. Too fast approaches or steep angles of attack result in damages to fenders, quay walls, or even impact the hull structure. We propose a shore-based, vessel-independent berthing assistant system to support sailors by Reference Points that are aligned to a quay's meter markings and identify the precise berthing location by measuring distance and approach speed. For this purpose, we define the concept of a Berthing Support Area (BSA), which specifies an area in which, subject to constraints, safe berthing is provided. Within this area there are Reference Points, perpendicular distance measurements at arbitrary positions, which are implemented with a set of LiDAR sensors that have been integrated into the quay wall. In a test campaign with a vessel equipped with DGPS sensors, we sailed seven different maneuvers and evaluated the precision and the accuracy of the Reference Points for speed and distance measurements. [ABSTRACT FROM AUTHOR]

The size of the global construction market is expected to increase to about $14 trillion by 2025, with fierce competition expected in emerging markets. However, construction is the only industrial sector whose productivity has declined over the past 50 years. Over the past two decades, the world economy has grown by 2.8% and manufacturing productivity has grown by 3.6% annually, but labor productivity growth in the construction sector has averaged less than 1%. Recently, construction companies have made various attempts to increase productivity, for example, by implementing 'smart construction' based on 3D data at all stages such as investigation, measurement, design, and construction. Many construction sites are increasing the utilization of building information modeling (BIM), laser scanners, unmanned aerial vehicles (UAVs), and automated construction equipment. However, studies on applying and analyzing 3D geospatial information in the construction process have been insufficient. In this study, an experiment on using 3D geospatial information technology in road construction was performed and analysis was conducted. The Global Navigation Satellite System (GNSS), static light detection and ranging (LiDAR), UAV LiDAR, and so forth were used to effectively build 3D geospatial information of the study area, and 3D designs were generated using the acquired data. The accuracy of data constructed through static LiDAR and UAV LiDAR was found to be within 0.025 m in the X, Y, and Z directions. A 3D design reflecting the actual terrain was created, and various sensors were installed on construction equipment to conduct experiments on construction automation. The productivity of construction automation was evaluated by comparing the results of traditional and automated construction processes. In the future, construction automation using 3D geospatial information technology will contribute to improving productivity not only for roads, but also at many other construction sites such as tunnels. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is the essential technique in mapping environments that are denied to the global navigation satellite systems (GNSSs), such as indoor spaces. In this article, we present a loop-closing continuous-time LIDAR-IMU SLAM for indoor environments. The design of the proposed SLAM is based on arbitrarily-oriented planar features that allow for point to plane matching for local but also global optimization. Moreover, to update the SLAM graph during the optimization, we propose a simple yet elegant loop closure method in the form of merging the planes together. Representing the SLAM map by planes is advantageous due to the abundant existence of planar structures in indoor built environments. The proposed method was validated on a specific configuration of three 2D LIDAR scanners mounted on a wearable platform (backpack). Scanned point clouds were compared against ones obtained from a commercial mobile mapping system (Viametris iMS3D) and a terrestrial laser scanner (RIEGL VZ-400). The experimental results demonstrate that our SLAM system is capable of mapping multi-storey buildings, staircases, cluttered areas and areas with curved walls. Furthermore, our SLAM system offers comparable performance to that of the commercial system as shown by the low deviation between the point clouds generated by both systems. The majority of the cloud-to-cloud absolute distances – about 92% – are less than 3 cm. [ABSTRACT FROM AUTHOR]

There is a growing need for 3D colored maps acquired from multi-sensor moving platforms. Accurate multi-sensor data alignment is an important prerequisite for the construction of 3D colored maps derived from simultaneously acquired camera and Light Detection and Ranging (LiDAR) data. However, current alignment methods are hampered by low automation, heavy computational costs or tedious system calibration set-ups. In this paper, we consider a LiDAR–global navigation satellite system (GNSS)/inertial navigation system (INS)–camera system mounted on an Unmanned Aerial Vehicle (UAV) platform. We present a detailed literature review of existing calibration methods for such systems. We propose a new versatile automatic and targetless calibration method of this system. This method involves estimating the calibration parameters by optimizing the correspondence between pairs of conjugate image points extracted from overlapping images and the projection of these points onto the georeferenced LiDAR point cloud. Experiments on actual data show the suitability of this method for the construction of 3D colored point clouds. Quantitative calibration results using checkpoints indicate that the obtained calibration accuracy is compatible with the accuracy of the georeferenced LiDAR point cloud, i.e. 5 cm. Further experiments on simulated data show the robustness of this approach to initial calibration parameters and low sensitivity to LiDAR point cloud density and noise. As this method is quite flexible, we believe it is more suitable for 3D color map generation than other methods proposed in the literature. [ABSTRACT FROM AUTHOR]

We present a new type of Light Detection And Ranging (LiDAR) sensor based on a variable lens pair prism. This system combines the principles of macro- and microscanners to exploit the advantages and eliminate most of the disadvantages of both system types. We describe the concept of this new LiDAR sensor and examine the system performance by simulation. Afterwards, we investigate the spot shape and beam profile with respect to the main system parameters such as the gap between lenses, the deflection angle, and the measurement distance. Furthermore, we present solutions to optimize the variable lens pair prism to achieve an ideal spot shape and beam profile in the entire field of view (FoV). [ABSTRACT FROM AUTHOR]

Point clouds are a very detailed and accurate vector data model of 3D geographic information. In contrast to other data models, no standard has been defined for visualization and management of point clouds based on levels of detail (LOD). This paper proposes the application of the concept of LODs to point clouds and defines the LOD0 for point cloud classification (the lowest possible level of detail) as urban and non-urban. A methodology based on the use of machine learning techniques is developed to perform LOD0 classification to airborne LiDAR data. Point clouds acquired with airborne laser scanner (ALS) are structured in grid maps and geometric features related with Z distribution and roughness are extracted from each cell. Six machine learning classifiers have been trained with datasets including urban (cities) and non-urban samples (farmlands and forests). The influence of grid size, point density, number of features and classifier type are analysed in detail. The classifiers have been tested in three case studies. The best results correspond to a grid size of 100 m and the use of 12 geometric features. The accuracy is around 90% in all tests and Cohen’s Kappa index reaches 81% in the best of cases. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative environment scan system (CESS) using multiple robots, where each robot has low-cost range finders and low processing power. To organize and maintain the CESS, a base robot monitors the positions of the child robots, controls them, and builds a map of the unknown environment, while the child robots with low performance range finders provide obstacle information. Even though each child robot provides approximated and limited information of the obstacles, CESS replaces the single LRF, which has a high cost, because much of the information is acquired and accumulated by a number of the child robots. Moreover, the proposed CESS extends the measurement boundaries and detects obstacles hidden behind others. To show the performance of the proposed system and compare this with the numerical models of the commercialized 2D and 3D laser scanners, simulation results are included.

Measurements of water surface topography are important for hydraulic structures, operation of hydropower plants as well as in the determination of water surface profiles in rivers, especially in the event of high waters. We therefore investigated the conditions at a confluence of two supercritical flows, where distinctly three-dimensional flow conditions of standing waves form, as well as an unsteady structure of the water flow in transversal and longitudinal directions. Due to the fast water surface dynamics and the phenomenon of foamed or two-phase flow, the conventional measurement methods typically used in hydro engineering are not suitable for capturing complex water surface topography with high temporal and spatial resolution. Hence we wanted to verify the appropriateness of the laser scanning method for water surface topography measurements. This measurement method, which is considered less suitable or even useless for measurements of water body surfaces, was, coupled with an innovative approach, successfully used for water surface measurements of dynamic, turbulent, two-phase water flow. The acquisition of a point cloud with high temporal and spatial resolution allows for the construction of topography of intensive waving, which will also enable a topology analysis based on a phenomenological analysis of the relations between integral parameters of water flows and standing wave characteristics at the confluence. [ABSTRACT FROM AUTHOR]

Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over 24 hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, weprovide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks, we visualize the learned features. [ABSTRACT FROM AUTHOR]

Vine vigour assessment has been a major concern of precision viticulture studies in order to identify areas of uniform vine performance within vineyards. Moreover, the counting and weighing of winter dormant canes is considered as the most informative measurement to indicate vine balance and is commonly performed manually by grape growers for management purposes. The main concern of this measurement is that it is time consuming and laborious and it cannot accommodate detailed sampling density. In the present study, the potential of using laser scanner technology as an automated, easy and rapid way to perform mapping of the winter pruning wood across the vineyard was investigated. The study was conducted during 2010 and 2011, in a one hectare commercial vineyard in central Greece, planted with cv. Agiorgitiko, a traditional Greek variety for the production of red wine. Parameters of topography, soil depth, soil texture, canopy properties (NDVI), yield, and grape quality were mapped and analysed in conjunction to winter canes weighing at pruning time. The mapping of the dormant canes was carried out using a 2D laser scanner sensor prior to pruning and manually measuring the pruning weight on a 10 9 20 m grid. Laser scanner measurements showed significant relationship in both 2010 and 2011 with pruning weight (r = 0.809 and r = 0.829 respectively, p < 0.001), yield and early season NDVI, showing the potential of using laser scanner measurements to assess variability in vine vigour within vineyards. These results suggest that laser scanners offer great promise to characterize within field variability in vine performance. [ABSTRACT FROM AUTHOR]

Point clouds provide valuable information that is not contained in satellite or aerial images. In this work, the potential of airborne LIDAR data for automatic land cover classification following the CORINE standard is evaluated. The methodology consists on the ordering of the point clouds by means of grid maps and rasterized for their use in the training of a Deep Learning classifier model ResNet-50. Three exclusive features of this type of information are extracted: height difference between points, average intensity and number of returns. The methodology has been tested in one case study at level 1 of CORINE inventory, reaching a 73.5% accuracy and a 59,8% Cohen Kappa coefficient. The main confusion occurs between types with strong similarities. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sensor technologies have been innovated and enhanced rapidly for highly automated vehicle and advanced driver assistance systems (ADAS) in automotive industry; however, in order to adopt sensors into mass production vehicle in near future, various requirements should be satisfied such as cost, durability, and maintainance without any loss of overall performance of the sensors. In this sense, a 3D flash lidar is one of primising range sensors because of no moving parts, compact package, and precise measure for distance by using a laser. In spite of the several advantages, the 3D flash lidar is not commonly used in automotive industry because it is quite expensive for adoption and it is manufactured with only general purpose currently; therefore, the cost reduction and optimal design to satisfy various purposes of ADAS or autonomous driving should be accomplished. In this paper, we propose a novel approach for design factor optimization of the 3D flash lidar based on a geometrical model by using structural similarity between the 3D flash lidar and 2D digital camera. In particular, focal length and area of a receiver (focal plane array and read-out integrated circuit) which directly affect on sensor performance (field of view and maximum detection range) are optimized as the design factors. From the optimization results in simulation, we show that optimal design factors according to various purposes required in ADAS could be easily determined and the sensor performances could be evaluated before manufacturing. It will reduce temporal and economic burdens for design and manufacturing in development process. [ABSTRACT FROM AUTHOR]

Si è conclusa da poco più di un mese la European Lidar Mapping Forum la conferenza dedicata al Laser Scanning e all’imaging realizzata nell’accog liente spazio del Passenger Terminal di Amsterdam. A report by the European Lidar Mapping Forum. The conference dedicated to the Laser Scanning and imaging wich took place in the Passenger Terminal of Amsterdam. The presentations and innovations in the exhibition gave an idea of the level of maturity of the technologies of 3D imaging, marking the level of the actual optimal use of technology capable of producing significant results, not experimental but absolutely acceptable by industrial and professional world, All this was preceded by three keynotes of great interest for the analysis of market potential, which attracted many visitors. The first relates to State of the Market: 3D Measurement. The second relates to The Current Status Regarding Civil Operations. The third concerns The Professional 3D Scanning World in 2020 - Evolution or Revolution?

In the procedures to minimize diachronic landslides, data on their temporal evolution and destructive capacities are necessary. For that purpose, remote-detection techniques proved to be highly useful for quantifying the ongoing change in the relief, as well as in comparisons between digital terrain models achieved by Light Detection and Ranging. The methodology presented in this paper includes the supervised merging and comparison of sequential scans, acquired within nearly annual intervals from an irregular terrain, which improves the quality of the results highlighting ground changes. This approach is based on the processing of digital terrain models from point clouds acquired by Terrestrial Laser Scanning to quantify and interpret the landslide displacements. In parallel, it is supported by Global Navigation Satellite Systems, the use of artificial targets and a refined data processing to minimize the uncertainty and improve the precision of the results. This is applied to a large translational slide affecting phyllite rocks in a IV-V degree of weathering settled on the southern slope of Sierra Nevada (south-eastern Spain). During the monitoring period (2008-2010), the slide remained inactive until 2009, followed by a reactivation with displacements in the range −1.80 to 1.20 m along the period 2009-2010, where negative values are downwards from the reference model (2009). The accumulated relative standard deviation between data sets was on the order of 7.5 cm, whereas the threshold to determine a terrain displacement (also avoiding changes due to erosion-accumulation processes) was of 10 cm. When applying this methodology to Airborne Laser Scanning datasets for the years 2008 and 2010, covering zones hidden to the line of sight of the terrestrial technique, a reactivation with similar deformation pattern was found useful to validate the findings, although the detail of changes and quantitative results did not match exactly due to the different accuracy and resolution of both techniques. The reactivation of the slide coincided with a period of intense rains, pointing to this as the triggering factor, with a precipitation threshold of roughly 1000 mm in a period of 4 months, only reached on one occasion throughout in the historical record. [ABSTRACT FROM AUTHOR]

This paper presents a concept of a laser scanner framework designed for obstacle avoidance used on mini fixed-wing unmanned aerial vehicles (UAVs) flying in outdoor environments. The innovation is a conical field of view that guarantees tri-dimensional (3D) obstacle detection and localization at any pitch or roll angle. This advantage is very important for the case of fixed-wing UAV flights where the attitude is changing rapidly. Measurement sequences create a map that is represented by a circular grid with the center fitted to the x-axis of the UAV's body, lying in the plane normal to the velocity vector and projected in the front of UAV. This means that the map cells contain differences between the safety zone radius and distances acquired from area in close proximity to the flight path. Actual UAV attitude can be compensated by rotation and shift of two masks of gains that are applied to the map to determine pitch and roll commands. Results of the simulation research conducted on the designed concept are very promising, as they present a combination of lateral and vertical obstacle avoidance. Based on the experience with laser rangefinders operating on a real UAV, it can be convincingly determined that the concept of the laser scanner is able to be brought into reality. [ABSTRACT FROM AUTHOR]

In precision agriculture, methods for analysing 3D point clouds of plants have been introduced, particularly pointing to the high accuracy of light detection and ranging (LiDAR) laser scanning under field conditions. In the present work, LiDAR-based 3D point clouds of cherry trees (n = 255) were analysed for estimating the leaf area as the main factor for water interception. Canopies were scanned for segmenting leaf area pointing to a high variability of canopy surface. The derived tree-specific data of leaf area index (LAI) were implemented into the Community Land Model (CLM), which takes into account canopy interception processes during rainfall events. During canopy development of perennial trees the LAI increased resulting in increased water interception. Events with low rain fall the interception reached 38–100 % capturing LAI of 0.76 – 2.11 m2/m2, respectively. In high rainfall events, interception varied 10–14 % capturing the same LAI range. An equation for describing the varying effects of rainfall intensity and LAI is proposed. The evapotranspiration and water interception data point to a substantial decrease of effective water supply that varies tree-individually during the season. In commercial fruit production, the proposed method can support precise irrigation management. • Method for analysing LAI of perennial fruit trees was employed. • CLM model was modified for calculating tree-wise water interception. • Seasonal course of tree-wise LAI can support precise irrigation management. • Water interception was a substantial factor for calculating gross water supply. [ABSTRACT FROM AUTHOR]

Airborne light detecting and ranging (LiDAR) systems have been widely used for the fast acquisition of dense topographic data. Regrettably, coordinate errors always exist in LiDAR-acquired points. The errors are attributable to several sources, such as laser ranging errors, sensor mounting errors, and position and orientation system (POS) systematic errors, among others. LiDAR strip adjustment (LSA) is the solution to eliminating the errors, but most state-of-the-art LSA methods neglect the influence from POS systematic errors by assuming that the POS is precise enough. Unfortunately, many of the LiDAR systems used in China are equipped with a low-precision POS due to cost considerations. Subsequently, POS systematic errors should be also considered in the LSA. This paper presents an aerotriangulation-aided LSA (AT-aided LSA) method whose major task is eliminating position and angular errors of the laser scanner caused by boresight angular errors and POS systematic errors. The aerial images, which cover the same area with LiDAR strips, are aerotriangulated and serve as the reference data for LSA. Two types of conjugate features are adopted as control elements (i.e., the conjugate points matched between the LiDAR intensity images and the aerial images and the conjugate corner features matched between LiDAR point clouds and aerial images). Experiments using the AT-aided LSA method are conducted using a real data set, and a comparison with the three-dimensional similarity transformation (TDST) LSA method is also performed. Experimental results support the feasibility of the proposed AT-aided LSA method and its superiority over the TDST LSA method. [ABSTRACT FROM PUBLISHER]

A report by the European Lidar Mapping Forum. The conference dedicated to the Laser Scanning and imaging wich took place in the Passenger Terminal of Amsterdam. The presentations and innovations in the exhibition gave an idea of the level of maturity of the technologies of 3D imaging, marking the level of the actual optimal use of technology capable of producing significant results, not experimental but absolutely acceptable by industrial and professional world, All this was preceded by three keynotes of great interest for the analysis of market potential, which attracted many visitors. The first relates to State of the Market: 3D Measurement. The second relates to The Current Status Regarding Civil Operations. The third concerns The Professional 3D Scanning World in 2020 - Evolution or Revolution? [ABSTRACT FROM AUTHOR]

Le tecniche di rilevamento tridimensionale con misurazione ad alta densità, ad esempio tramite approcci LiDARaerotrasportati, da terra (TLS - Terrestrial Laser Scanner) o su mezzi mobili (mobile mapping), permettono di rilevaree osservare il territorio con una accuratezza, una risoluzione e una ricchezza di dettaglio globalmente molto elevaterispetto agli approcci di rilevamento tradizionali. Per la gestione delle grandi moli di dati generati si presenta unaapplicazione di una tecnologia altamente innovativa, unica a livello internazionale, sviluppata dall’Università degli Studidi Brescia, in accordo con l’azienda spin-off Gexcel srl, che permette la condivisione e visualizzazione di dati ad altadensità, anche via rete. Abstract 3D surveying techniques dealing withhigh density measurement (i.e. throughairborne LiDAR by TLS - Terrestrial LaserScanner - or by mobile mapping)allow to survey and observe the territorywith accuracy, a particular resolutionand richness of details extremely highcompared to traditional survey approaches.These technologies, due tothe size of raw data, not allow the sharingand the direct reading of 3D databy different users. Usually, the data areput into standard formats (digital terrainmodel, contour lines, etc.) to beshared. The raw data (with all the associatedcontents) are, on the otherhand, generally lost or saved in formatsand devices (hard disks or DVD) thatdoesn’t make it available to the costumer.An application of a technologydeveloped by the University of Brescia(together with the spin-off Gexcel srl) ispresented. This application allows thesharing and visualization of these databy web. The difficulty of implementingthe sharing technologies of LiDARdata lies in the transfer of 3D surveyeddata, even if only to display them. Thisresearch intends to propose a new wayof data managing/transmission, also byinternet, enabling to store 3D LiDARdata in a single file and to add differentlayers on this model. In particular,the research shows how in a file - ona remote server - it is possible to store3D multi-resolution data about landslidesof the Veneto Region. These 3Ddata (enriched by different layers) canbe visualized, downloaded, queriedand shared via web. It also shows howit can be displayed on 3D model, informationlevels from GIS that analyzedata of hydrological risk. The researchproposes a first application of LiDARdata web sharing applied to studiesof hydrological disasters of the VenetoRegion. This is intended as an exampleof a useful technology to support allthe activities for rapid decisions.

In the last decades, several studies have been conducted aiming to the extraction of forest variables from LiDAR data. Although such studies have indicated great potential, the high cost associated with LiDAR data acquisition process inhibits the technology to become an operational technique for forestry applications. The cost of a LiDAR survey, as for any other data collection techniques, is composed of fixed and variable costs. The variable portion, which can be optimized, is dependent, among other factors, on the number of flight hours. The flight time is mainly dependent on the flight configuration used for the survey. The objective of this paper is to investigate the impact of using different operational parameters on different species of forest plantations, to provide inputs for an adequate cost-benefit analysis. The different configurations are evaluated in terms of the number of individual trees automatically detected, individual height and volume, using the forest inventory as the reference data. The experiments have shown that compatible results are obtained using different configurations with flight time varying by a factor of 3.5 to 10 times. Also, for a given point density, preference should be given to the configuration based on a lower flying height. [ABSTRACT FROM AUTHOR]

Grazie alla geomatica e alle tecnologie più avanzate è stato ricostruito in poche ore un rilievo tridimensionale - sopra e sott’acqua - dello scafo e del fondale relativo al naufragio della nave Costa Concordia utilizzando un sonar Multibeam Reson 7125 e un laser scanner Optech ILRIS HD con interventi condotti in condizioni di estrema sicurezza, garantendo la protezione sia dei soccorritori che del delicato ambiente naturale. Geomatics assists early intervention on the Costa Concordia Thanks to the Geomatics, the Costa Concordia operations are conducted in conditions of extreme security to protect both the lives of rescue workers and the delicate environment.All technologies involved (Lidar, Multibeam, GNSS, INS, ecc…), while having a primary role in real life, have always worked quietly behind the scenes.

Highlights: [•] Positioning algorithm for arms of variable geometry orchard sprayer was developed. [•] During spraying the canopy is continuously measured by a laser scanner. [•] Positioning algorithm uses laser scanner measurements for contour determination. [•] Spraying arms move according to canopy contour. [Copyright &y& Elsevier]

Abstract: Scanning hyperspectral systems based on laser induced fluorescence (LIF) have been developed and realized at the ENEA allowing to obtain information of analytical and qualitative interest on different materials by the study of the emission of fluorescence. This technique, for a surface analysis, is fast, remote, not invasive and specific. A new compact setup capable of fast 2D monochromatic images acquisition on up to 90 different spectral channels in the visible/UV range will be presented. It has been recently built with the aim to increase the performances in terms of space resolution, time resolved capabilities and data acquisition speed. Major achievements have been reached by a critical review of the optical design. The results recently obtained with in-situ measurements of interest for applications in the field of cultural heritage will be shown. © 2001 Elsevier Science. All rights reserved [Copyright &y& Elsevier]

Rich geometric models of the environment are needed for robots to carry out their missions. However a robot operating in a large environment would require a compact representation. In this article, we present a method that relies on the idea that a plane appears as a line segment in a 2D scan, and that by tracking those lines frame after frame, it is possible to estimate the parameters of that plane. The method is divided in three steps: fitting line segments on the points of the 2D scan, tracking those line segments in consecutive scan and estimating the parameters with a graph based SLAM (Simultaneous Localisation And Mapping) algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed. [ABSTRACT FROM AUTHOR]

Abstract: A lack of reliable observations for canopy science research is being partly overcome by the gradual use of lidar remote sensing. This study aims to improve lidar-based canopy characterization with airborne laser scanners through the combined use of lidar composite metrics and machine learning models. Our so-called composite metrics comprise a relatively large number of lidar predictors that tend to retain as much information as possible when reducing raw lidar point clouds into a format suitable as inputs to predictive models of canopy structural variables. The information-rich property of such composite metrics is further complemented by machine learning, which offers an array of supervised learning models capable of relating canopy characteristics to high-dimensional lidar metrics via complex, potentially nonlinear functional relationships. Using coincident lidar and field data over an Eastern Texas forest in USA, we conducted a case study to demonstrate the ubiquitous power of the lidar composite metrics in predicting multiple forest attributes and also illustrated the use of two kernel machines, namely, support vector machine and Gaussian processes (GP). Results show that the two machine learning models in conjunction with the lidar composite metrics outperformed traditional approaches such as the maximum likelihood classifier and linear regression models. For example, the five-fold cross validation for GP regression models (vs. linear/log-linear models) yielded a root mean squared error of 1.06 (2.36) m for Lorey''s height, 0.95 (3.43) m for dominant height, 5.34 (8.51) m2/ha for basal area, 21.4 (40.5) Mg/ha for aboveground biomass, 6.54 (9.88) Mg/ha for belowground biomass, 0.75 (2.76) m for canopy base height, 2.2 (2.76) m for canopy ceiling height, 0.015 (0.02) kg/m3 for canopy bulk density, 0.068 (0.133) kg/m2 for available canopy fuel, and 0.33 (0.39) m2/m2 for leaf area index. Moreover, uncertainty estimates from the GP regression were more indicative of the true errors in the predicted canopy variables than those from their linear counterparts. With the ever-increasing accessibility of multisource remote sensing data, we envision a concomitant expansion in the use of advanced statistical methods, such as machine learning, to explore the potentially complex relationships between canopy characteristics and remotely-sensed predictors, accompanied by a desideratum for improved error analysis. [Copyright &y& Elsevier]

This paper presents a low-cost true color terrestrial laser scanning system, described in terms of the hardware and software elements necessary to add color capability to existing non-color laser scanners. A purpose-built camera mount allows a digital camera to be positioned coincident and collinear with the beam detector of the laser scanner device, such that mismatch between color data and laser scan points is minimized. An analytic mapping implemented in a Matlab toolbox registers the photographs, after rectification, with the laser scan point cloud, by solving for two tie-points per image. The resulting true color point clouds are accurate and easily interpretable. The adjustable camera mount fits a range of cameras and, with minor adaptation, could be used with different laser scanner devices. A detailed error analysis will allow comparisons to be drawn with alternative technologies, such as photogrammetry and commercial true color laser scanners. This low-cost, accurate, and flexible true color laser scanning technology has the potential to make a significant improvement to existing methods of spatial data acquisition. [ABSTRACT FROM AUTHOR]

Determination of fracture orientation can be an important aspect of structural analysis in reservoir characterization. The availability of ground-based laser scanner systems opens up new possibilities for the determination of fracture surface orientation in rock outcrops. Scanners are available in low-sample-density, low-accuracy, and fast, high-sample-density, high-accuracy models. These automatic laser scanner systems produce enormous volumes or "clouds" of point data at an instrument dependent accuracy and resolution, which can be at the millimeter level. This huge volume of data calls for an automated and objective method of analysis. We have developed a surface classification algorithm based on a multipass partitioning of the point cloud. The method makes use of both spatial proximity and the orientation of an initial coarse-grained model of the point cloud. Unsupervised classification of surface sets is demonstrated herein using the new algorithm. Both previously mentioned types of scanners have been used to map the Jackfork sandstone outcrop at Big Rock Quarry in Little Rock, Arkansas. We apply the surface classification algorithm to these data to extract fracture surface orientations from the point cloud. The effectiveness of these new technologies when applied to fracture analysis is clearly demonstrated in this example. It is also shown that the low-density, low-resolution type of scanner is adequate to define general geomorphology but is inadequate for fracture definition. The surface classification algorithm can be used to reliably extract fracture and bedding strike and dip angles from the three-dimensional point locations acquired using centimeter-accurate, high density laser scanner systems. [ABSTRACT FROM AUTHOR]