Laser Imaging Detection And Ranging Research Articles

Pesticide contamination of freshwater ecosystems: mapping vulnerable areas and mitigation scenarios in the Prosecco DOCG wine production area

Freshwater ecosystems are the most vulnerable environments worldwide and the most biodiverse, providing essential ecosystem services. The role of land management in agriculture is paramount with the dramatic increase in pesticides: two million tonnes used worldwide (47.5% herbicides, 29.5% insecticides, and 17.5% fungicides) are jeopardising freshwater ecosystems. Concerns about the risk of pesticide contamination from viticulture have led to implementing nature-based mitigation measures (buffer strips and hedgerows) and technical improvements. The general aim is to assess spatial proximity among vineyards and river networks within the Prosecco denominazione di origine controllata (DOCG) area to identify potential critical areas for pesticide contamination. Specific objectives are: (a) mapping vineyards within the Prosecco DOCG area, (b) identifying river banks with a higher probability of experiencing pesticide contamination, and (c) mapping critical areas potentially affected by pesticide contamination. Spatial modelling was based on very high geometric resolution ortophotos (0.5 m), laser imaging, detection, and ranging (LiDAR) data (1 m), and morpho-hydrological parameters of the river network. Proximity and morpho-hydrological modelling showed that due to little distance from Prosecco croplands (5–20 m), freshwater ecosystems may be affected in different basins by spray drift pesticide contamination. Distances between vineyards and streams were shown to be critical, as 35.7% and 13.9% of river banks were within 20 m and 5 m distance from vineyards, respectively. Furthermore, 52% of basins presented river banks intersecting vineyards at 5 m, while 37% were within 20 m distance. Such hotspots should be investigated in the field for watershed-based quality assessment. However, mitigation scenarios indicate that spray drift contamination might be reduced by 75%, minimising the effect from 20 m to 5 m distance from vineyards and, therefore, avoiding reaching part of riparian and aquatic ecosystems. Geovisualisation of river banks proximity at watershed level offered insight into area with high probability of experiencing pesticide contamination from vineyards due to spray drift.

Biometric recognition through gait analysis

The use of people recognition techniques has become critical in some areas. For instance, social or assistive robots carry out collaborative tasks in the robotics field. A robot must know who to work with to deal with such tasks. Using biometric patterns may replace identification cards or codes on access control to critical infrastructures. The usage of Red Green Blue Depth (RGBD) cameras is ubiquitous to solve people recognition. However, this sensor has some constraints, such as they demand high computational capabilities, require the users to face the sensor, or do not regard users’ privacy. Furthermore, in the COVID-19 pandemic, masks hide a significant portion of the face. In this work, we present BRITTANY, a biometric recognition tool through gait analysis using Laser Imaging Detection and Ranging (LIDAR) data and a Convolutional Neural Network (CNN). A Proof of Concept (PoC) has been carried out in an indoor environment with five users to evaluate BRITTANY. A new CNN architecture is presented, allowing the classification of aggregated occupancy maps that represent the people’s gait. This new architecture has been compared with LeNet-5 and AlexNet through the same datasets. The final system reports an accuracy of 88%.

BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin

Modeling the photovoltaic (PV) energy output with high accuracy is essential for predicting and analyzing the performance of a PV system. In the particular cases of building-integrated and building-attached photovoltaic systems (BIPV and BAPV, respectively) the time-varying partial shading conditions are a relevant added difficulty for modeling the PV power conversion. The availability of laser imaging detection and ranging (LIDAR) data to create very-high-resolution elevation digital models can be effectively used for computing the shading at high resolution. In this work, an artificial neural network (ANN) has been used to model the power generation of different BIPV arrays on a 5 min basis using the meteorological and solar irradiance on-site conditions, as well as the shading patterns estimated from a digital surface model as inputs. The ANN model has been validated using three years of 5-min-basis monitored data showing very high accuracy (6–16% of relative error depending on the façade). The proposed methodology combines the shading computation from a digital surface model with powerful machine learning algorithms for modeling vertical PV arrays under partial shading conditions. The results presented here prove also the capability of the machine learning techniques towards the creation of a digital twin for the specific case of BIPV systems that complements the conventional monitoring strategies and can be used in the diagnosis of performance anomalies.

3D environmental urban BIM using LiDAR data for visualisation on Google Earth

Digitisation and building information modelling (BIM) technology are being adopted in the construction industry, but their application in the sustainable development of urban planning and civil engineering has been limited. A methodology was developed to generate 3D urban maps, which represent a potential environmental exposure related to urban green infrastructure. The methodology incorporated 3D geometric data obtained by Laser Imaging Detection and Ranging (LiDAR), location and information of green infrastructure, and meteorological data (direction and velocity of wind) into a single 3D BIM model to estimate the potential levels of exposure to urban green infrastructure by applying a modified aerobiological index to create risk maps for ornamental trees (AIROT). The BIM model facilitated a 3D visualisation of the exposure in urban environments by exporting information to GIS software, which could then be visualised on Google Earth. The main advance of the developed methodology is its applicability to any urban environment for determining and reducing aerobiological health risks in civil engineering.

Design and Implementation of an Automatic Control System using Motors and Control Sensors to Stabilize a Horizontal Platform with a LiDAR Sensor

Laser imaging detection and ranging (LiDAR) sensors can be used in numerous applications mainly due to their ability to measure distances or perform 3D surface mappings. However, to perform these functions efficiently in certain situations, such as in a moving vehicle, LiDAR sensors require a stabilizing platform to prevent terrain irregularities from affecting their measurements. Moreover, accurate platform control is one of the main challenges of modern control engineering. Although several approaches concerning this challenge have been proposed, the high computational cost of control algorithms continues to be their largest drawback. Within this context, the present work seeks to implement a control system to achieve the horizontal stabilization of a two-degree-of-freedom platform for a LiDAR sensor using inertial systems and embedded software platforms, thereby guaranteeing not only portability but also low computational costs.

Integration of Aerobiological Information for Construction Engineering Based on LiDAR and BIM

In green urban areas, the allergenic factor is important when selecting trees to improve the quality of life of the population. An application of laser imaging detection and ranging (LiDAR) in building information modelling (BIM) is the capture of geo-referenced geometric information of the environment. This study presents the process of digitalisation of a green infrastructure inventory based on the geolocation and bioparameters of the cypress species. The aerobiological index (IUGZA) was estimated by developing green infrastructure BIM models at different detail levels and with a new BIM dimension (6D) for the urban environment. The novelty of the study is the modelling of urban information for evaluating the potential environmental impact related to the allergenicity of the urban green infrastructure using LiDAR through BIM. The measurements of cypress trees based on bioparameters and distances were applied to the IUGZA. This innovation for describing the current 3D environments and designing new scenarios in 6D may prevent future problems in urban areas during construction projects.

GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

Using Geomatic Techniques to Estimate Volume–Area Relationships of Watering Ponds

Watering ponds represent an important part of the hydrological resources in some water-limited environments. Knowledge about their storage capacity and geometrical characteristics is crucial for a better understanding and management of water resources in the context of climate change. In this study, the suitability of different geomatic approaches to model watering pond geometry and estimate pond-specific and generalized volume–area–height (V–A–h) relationships was tested. Terrestrial structure-from-motion and multi-view-stereo photogrammetry (SfM-MVS), terrestrial laser scanner (TLS), laser-imaging detection and ranging (LIDAR), and aerial SfM-MVS were tested for the emerged terrain, while the global navigation satellite system (GNSS) was used to survey the submerged terrain and to test the resulting digital elevation models (DEMs). The combined use of terrestrial SfM-MVS and GNSS produced accurate DEMs of the ponds that resulted in an average error of 1.19% in the maximum volume estimation, comparable to that obtained by the TLS+GNSS approach (3.27%). From these DEMs, power and quadratic functions were used to express pond-specific and generalized V–A–h relationships and checked for accuracy. The results revealed that quadratic functions fit the data particularly well (R2 ≥ 0.995 and NRMSE < 2.25%) and can therefore be reliably used as simple geometric models of watering ponds in hydrological simulation studies. Finally, a generalized V–A power relationship was obtained. This relationship may be a valuable tool to estimate the storage capacity of other watering ponds in comparable areas in a context of data scarcity.

Tests with SAR Images of the PAZ Platform Applied to the Archaeological Site of Clunia (Burgos, Spain)

This article presents the first results obtained from the use of high-resolution images from the SAR-X sensor of the PAZ satellite platform. These are in result of the application of various radar image-treatment techniques, with which we wanted to carry out a non-invasive exploration of areas of the archaeological site of Clunia (Burgos, Spain). These areas were analyzed and contrasted with other sources from high-resolution multispectral images (TripleSat), or from digital surface models obtained from Laser Imaging Detection and Ranging (LiDAR) data from the National Plan for Aerial Orthophotography (PNOA), and treated with image enhancement functions (Relief Visualization Tools (RVT)). Moreover, they were compared with multispectral images created from the Infrared Red Blue (IRRB) data contained in the same LiDAR points.

Camera-LiDAR Multi-Level Sensor Fusion for Target Detection at the Network Edge.

There have been significant advances regarding target detection in the autonomous vehicle context. To develop more robust systems that can overcome weather hazards as well as sensor problems, the sensor fusion approach is taking the lead in this context. Laser Imaging Detection and Ranging (LiDAR) and camera sensors are two of the most used sensors for this task since they can accurately provide important features such as target´s depth and shape. However, most of the current state-of-the-art target detection algorithms for autonomous cars do not take into consideration the hardware limitations of the vehicle such as the reduced computing power in comparison with Cloud servers as well as the reduced latency. In this work, we propose Edge Computing Tensor Processing Unit (TPU) devices as hardware support due to their computing capabilities for machine learning algorithms as well as their reduced power consumption. We developed an accurate and small target detection model for these devices. Our proposed Multi-Level Sensor Fusion model has been optimized for the network edge, specifically for the Google Coral TPU. As a result, high accuracy results are obtained while reducing the memory consumption as well as the latency of the system using the challenging KITTI dataset.

Rapid Development of a Mobile Robot for the Nakanoshima Challenge Using a Robot for Intelligent Environments

Automated mobile platforms are commonly used to provide services for people in an intelligent environment. Data on the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. Therefore, automated mobile robots are required to reconstruct location data in surveillance tasks. This paper describes the development of an autonomous mobile robot to achieve tasks in intelligent environments. In particular, the robot constructed route maps in outdoor environments using laser imaging detection and ranging (LiDAR), and RGB-D sensors via simultaneous localization and mapping. The mobile robot system was developed based on a robot operating system (ROS), reusing existing software. The robot participated in the Nakanoshima Challenge, which is an experimental demonstration test of mobile robots in Osaka, Japan. The results of the experiments and outdoor field tests demonstrate the feasibility of the proposed robot system.

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

Use of Remote Sensing Tools to Measure a Fluvial Geomorphic Design-Input Parameter for Land Reclamation

Fluvial geomorphic approaches for reclamation landform design have been applied since 2000, mostly in mined lands, as an alternative to conventional landform design methods. Those approaches aim to reconstruct mature landforms and drainage networks that would develop within a natural catchment, after thousands of years of work performed by geomorphic processes. Some fluvial geomorphic design methods take specific measurements from natural and stable reference areas for initial input values for reclamation design. Valid reference areas can be difficult to find, can be in highly anthropized environments, or may be difficult to access. This paper evaluates the use of remote sensing tools to measure morphometric parameters in upper sections of agricultural land catchments considered for use as reference areas. The ridge to head of channel distance (Xrh) was the parameter of interest. We used land surface profiles developed from LiDAR (Laser Imaging Detection and Ranging) data and planimetric measurements from orthoimages to estimate Xrh. The results obtained by the two methods were encouraging but showed a significant difference. Ground truthing showed that ploughing obliterated between 19.5 and 22.4 m (on average) of the headwater section of first-order channels, reducing the channel length by 15.1 to 32.4%. Using a greater Xrh value than appropriate for near steady-state conditions as a design input for a geomorphic reclamation project would be expected to result in active erosion processes in the constructed reclamation to regain their missing channel length. We recognize the advantages and limitations of remote sensing methods for measuring the morphometric parameters of the landform relief design inputs. We show how these tools may be used to help select and prioritize reference areas, and warn about the use of disturbed landscapes as reference areas to assure the geomorphic stability of the constructed reclamation designs.

Remote Sensing, Archaeological, and Geophysical Data to Study the Terramare Settlements: The Case Study of Fondo Paviani (Northern Italy)

During the Middle and Recent Bronze Age, the Po Plain and, more broadly Northern Italy were populated by the so-called “Terramare”, embanked settlements, surrounded by a moat. The buried remains of these archaeological settlements are characterized by the presence of a system of palaeo-environments and a consequent natural gradient in soil moisture content. These differences in the soil are often firstly detectable on the surface during the seasonal variations, with aerial, satellite, and Laser Imaging Detection and Ranging (LIDAR) images, without any information on the lateral and in-depth extension of the related buried structures. The variation in the moisture content of soils is directly related to their differences in electrical conductivity. Electrical resistivity tomography (ERT) and frequency domain electromagnetic (FDEM), also known as electromagnetic induction (EMI) measurements, provide non-direct measurements of electrical conductivity in the soils, helping in the reconstruction of the geometry of different buried structures. This study presents the results of the multidisciplinary approach adopted to the study of the Terramare settlement of Fondo Paviani in Northern Italy. Remote sensing and archaeological data, collected over about 10 years, combined with more recent ERT and FDEM measurements, contributed to the analysis of this particular, not yet wholly investigated, archaeological site. The results obtained by the integrated multidisciplinary study here adopted, provide new useful, interesting information for the archaeologists also suggesting future strategies for new studies still to be conducted around this important settlement.

Path Planner for Autonomous Exploration of Underground Mines by Aerial Vehicles.

This paper presents a path planner solution that makes it possible to autonomously explore underground mines with aerial robots (typically multicopters). In these environments the operations may be limited by many factors like the lack of external navigation signals, the narrow passages and the absence of radio communications. The designed path planner is defined as a simple and highly computationally efficient algorithm that, only relying on a laser imaging detection and ranging (LIDAR) sensor with Simultaneous localization and mapping (SLAM) capability, permits the exploration of a set of single-level mining tunnels. It performs dynamic planning based on exploration vectors, a novel variant of the open sector method with reinforced filtering. The algorithm incorporates global awareness and obstacle avoidance modules. The first one prevents the possibility of getting trapped in a loop, whereas the second one facilitates the navigation along narrow tunnels. The performance of the proposed solution has been tested in different study cases with a Hardware-in-the-loop (HIL) simulator developed for this purpose. In all situations the path planner logic performed as expected and the used routing was optimal. Furthermore, the path efficiency, measured in terms of traveled distance and used time, was high when compared with an ideal reference case. The result is a very fast, real-time, and static memory capable algorithm, which implemented on the proposed architecture presents a feasible solution for the autonomous exploration of underground mines.

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.

Upscaling UAS Paradigm to UltraLight Aircrafts: A Low-Cost Multi-Sensors System for Large Scale Aerial Photogrammetry

The use of unmanned aerial systems (UASs) has rapidly grown in many civil applications since the early 2010s. Nowadays, a large variety of reliable low-cost UAS sensors and controllers are available. However, contrary to ultralight aircrafts (ULAs), UASs have a too small operational range to efficiently cover large areas. Flight regulations prevailing in many countries further reduced this operational range; in particular, the “within visual line of sight” rule. This study presents a new system for image acquisition and high-quality photogrammetry of large scale areas (>10 km²). It was developed by upscaling the UAS paradigm, i.e., low-cost sensors and controllers, little (or no) on-board active stabilization, and adequate structure from motion photogrammetry, to an ULA platform. Because the system is low-cost (good quality-price ratio of UAS technologies), multi-sensors (large variety of available UAS sensors) and versatile (high ULA operational flexibility and more lenient regulation than for other platforms), the possible applications are numerous in miscellaneous research domains. The system was described in detail and illustrated from the flight and images acquisition to the photogrammetric routine. The system was successfully used to acquire high resolution and high quality RGB and multispectral images, and produced precisely georeferenced digital elevation model (DEM) and orthophotomosaics for a forested area of 1200 ha. The system can potentially carry any type of sensors. The system compatibility with any sensor can be tested, in terms of image quality and flight plan, with the proposed method. This study also highlighted a major technical limitation of the low-cost thermal infrared cameras: the too high integration time with respect to the flight speed of most UASs and ULAs. By providing the complete information required for reproducing the system, the authors seek to encourage its implementation in different geographical locations and scientific contexts, as well as, its combination with other sensors, in particular, laser imaging detection and ranging (LiDAR) and hyperspectral.

Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services

There is an urgent need for holistic tools to assess the health impacts of climate change mitigation and adaptation policies relating to increasing public green spaces. Urban vegetation provides numerous ecosystem services on a local scale and is therefore a potential adaptation strategy that can be used in an era of global warming to offset the increasing impacts of human activity on urban environments. In this study, we propose a set of urban green ecological metrics that can be used to evaluate urban green ecosystem services. The metrics were derived from two complementary surveys: a traditional remote sensing survey of multispectral images and Laser Imaging Detection and Ranging (LiDAR) data, and a survey using proximate sensing through images made available by the Google Street View database. In accordance with previous studies, two classes of metrics were calculated: greenery at lower and higher elevations than building facades. In the last phase of the work, the metrics were applied to city blocks, and a spatially constrained clustering methodology was employed. Homogeneous areas were identified in relation to the urban greenery characteristics. The proposed methodology represents the development of a geographic information system that can be used by public administrators and urban green designers to create and maintain urban public forests.

High-Resolution Ultrasound Sensing for Robotics Using Dense Microphone Arrays

State-of-the-art autonomous vehicles use all kinds of sensors based on light, such as a camera or LIDAR (Laser Imaging Detection And Ranging). These sensors tend to fail when exposed to airborne particles. Ultrasonic sensors have the ability to work in these environments since they have longer wavelengths and are based on acoustics, making them able to pass through the mentioned distortions. However, they have a lower angular resolution compared to their optical counterparts. In this article a 3D in-air sonar sensor is simulated, consisting of a Uniform Rectangular Array similar to the newly developed micro Real Time Imaging Sonar ( $\mu $ RTIS) by CoSys-Lab. Different direction of arrival techniques will be compared for an 8 by 8 uniform rectangular microphone array in a simulation environment to investigate the influence of different parameters in a completely controlled environment. We will investigate the influence of the signal-to-noise ratio and number of snapshots to the angular and spatial resolution in the direction parallel and perpendicular to the direction of the emitted signal, respectively called the angular and range resolution. We will compare these results with real-life imaging results of the $\mu $ RTIS. The results presented in this article show that, despite the fact that in-air sonar applications are limited to only one snapshot, more advanced algorithms than Delay-And-Sum beamforming are viable options, which is confirmed with the real-life data captured by the $\mu $ RTIS.

People Detection and Tracking Using LIDAR Sensors

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.