Filtrage et classification de nuage de points sur la base d'attributs morphologiques

International audienc

Using AI and Robotics for EV battery cable detection.: Development and implementation of end-to-end model-free 3D instance segmentation for industrial purposes

Master's thesis in Information- and communication technology (IKT590)This thesis describes a novel method for capturing point clouds and segmenting instances of cabling found on electric vehicle battery packs. The use of cutting-edge perception algorithm architectures, such as graph-based and voxel-based convolution, in industrial autonomous lithium-ion battery pack disassembly is being investigated. The thesis focuses on the challenge of getting a desirable representation of any battery pack using an ABB robot in conjunction with a high-end structured light camera, with "end-to-end" and "model-free" as design constraints. The thesis employs self-captured datasets comprised of several battery packs that have been captured and labeled. Following that, the datasets are used to create a perception system. This thesis recommends using HDR functionality in an industrial application to capture the full dynamic range of the battery packs. To adequately depict 3D features, a three-point-of-view capture sequence is deemed necessary. A general capture process for an entire battery pack is also presented, but a next-best-scan algorithm is likely required to ensure a "close to complete" representation. Graph-based deep-learning algorithms have been shown to be capable of being scaled up to50,000inputs while still exhibiting strong performance in terms of accuracy and processing time. The results show that an instance segmenting system can be implemented in less than two seconds. Using off-the-shelf hardware, demonstrate that a 3D perception system is industrially viable and competitive with a 2D perception system

Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes

Key message We present a data-driven technique to visualize forest landscapes and simulate their future development according to alternative management scenarios. Gentle harvesting intensities were preferred for maintaining scenic values in a test of eliciting public's preferences based on the simulated landscapes. Context Visualizations of future forest landscapes according to alternative management scenarios are useful for eliciting stakeholders' preferences on the alternatives. However, conventional computer visualizations require laborious tree-wise measurements or simulators to generate these observations. Aims We describe and evaluate an alternative approach, in which the visualization is based on reconstructing forest canopy from sparse density, leaf-off airborne laser scanning data. Methods Computational geometry was employed to generate filtrations, i.e., ordered sets of simplices belonging to the three-dimensional triangulations of the point data. An appropriate degree of filtering was determined by analyzing the topological persistence of the filtrations. The topology was further utilized to simulate changes to canopy biomass, resembling harvests with varying retention levels. Relative priorities of recreational and scenic values of the harvests were estimated based on pairwise comparisons and analytic hierarchy process (AHP). Results The canopy elements were co-located with the tree stems measured in the field, and the visualizations derived from the entire landscape showed reasonably realistic, despite a low numerical correspondence with plot-level forest attributes. The potential and limitations to improve the proposed parameterization are discussed. Conclusion Although the criteria to evaluate the landscape visualization and simulation models were not conclusive, the results suggest that forest scenes may be feasibly reconstructed based on data already covering broad areas and readily available for practical applications.Peer reviewe

Interactive inspection of complex multi-object industrial assemblies

The final publication is available at Springer via http://dx.doi.org/10.1016/j.cad.2016.06.005The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.Peer ReviewedPostprint (author's final draft

Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data

[EN] The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75¿m square subplots and circular plots with a radius of 15¿m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2¿=¿0.957, maximum height: R2¿=¿0.771, cover: R2¿=¿0.871, and volume: R2¿=¿0.951). The proportion of explained variance was slightly lower at 3.75¿m side cells (mean height: R2¿=¿0.633, maximum height: R2¿=¿0.470, cover: R2¿=¿0.581, and volume R2¿=¿0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.This research was developed mainly in the Integrated Remote Sensing Studio (IRSS) of University of British Columbia (UBC) (Canada) as a result of the Erasmus + KA-107 mobility grant. The authors thank the financial support provided by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Crespo-Peremarch, P.; Tompalski, P.; Coops, N.; Ruiz Fernández, LÁ. (2018). Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sensing of Environment. 217:400-413. https://doi.org/10.1016/j.rse.2018.08.033S40041321

Processing and analysis of airborne fullwaveform laser scanning data for the characterization of forest structure and fuel properties

Tesis por compendio[ES] Esta tesis aborda el desarrollo de métodos de procesado y análisis de datos ALSFW para la caracterización de la estructura vertical del bosque y, en particular, del sotobosque. Para responder a este objetivo general, se establecieron seis objetivos específicos: En primer lugar, se analiza la influencia de la densidad de pulso, de los parámetros de voxelización (tamaño de vóxel y valor de asignación) y de los métodos de regresión sobre los valores de las métricas ALSFW y sobre la estimación de atributos de estructura del bosque. Para ello, se redujo aleatoriamente la densidad de pulsos y se modificaron los parámetros de voxelización, obteniendo los valores de las métricas ALSFW para las diferentes combinaciones de parámetros. Estas mismas métricas ALSFW se emplearon para la estimación de atributos de la estructura del bosque mediante diferentes métodos de regresión. En segundo lugar, se integran métodos de procesado y análisis de datos ALSFW en una nueva herramienta llamada WoLFeX (Waveform Lidar for Forestry eXtraction) que incluye los procesos de recorte, corrección radiométrica relativa, voxelización y extracción de métricas a partir de los datos ALSFW, así como nuevas métricas descriptoras del sotobosque. En tercer lugar, se evalúa la influencia del ángulo de escaneo utilizado en la adquisición de datos ALS y la corrección radiométrica en la extracción de métricas ALSFW y en la estimación de atributos de combustibilidad forestal. Para ello, se extrajeron métricas ALSFW con y sin corrección radiométrica relativa y empleando diferentes ángulos de escaneo. En cuarto lugar, se caracteriza la oclusión de la señal a lo largo de la estructura vertical del bosque empleando y comparando tres tipos diferentes de láser escáner (ALSFW, ALSD y láser escáner terrestre: TLS, por sus siglas en inglés), determinando así sus limitaciones en la detección de material vegetativo en dos ecosistemas forestales diferenciados: el boreal y el mediterráneo. Para cuantificar la oclusión de la señal a lo largo de la estructura vertical del bosque se propone un nuevo parámetro, la tasa de reducción del pulso, basada en el porcentaje de haces láser bloqueados antes de alcanzar una posición dada. En quinto lugar, se evalúa la forma en que se detectan y determinan las clases de densidad de sotobosque mediante los diferentes tipos de ALS. Se compararon los perfiles de distribución vertical en los estratos inferiores descritos por el ALSFW y el ALSD con respecto a los descritos por el TLS, utilizando este último como referencia. Asimismo, se determinaron las clases de densidad de sotobosque aplicando la curva Lorenz y el índice Gini a partir de los perfiles de distribución vertical descritos por ALSFW y ALSD. Finalmente, se aplican y evalúan las nuevas métricas ALSFW basadas en la voxelización, utilizando como referencia los atributos extraídos a partir del TLS, para estimar la altura, la cobertura y el volumen del sotobosque en un ecosistema mediterráneo.[EN] This thesis addresses the development of ALSFW processing and analysis methods to characterize the vertical forest structure, in particular, the understory vegetation. To answer this overarching goal, a total of six specific objectives were established: Firstly, the influence of pulse density, voxel parameters (i.e., voxel size and assignation value) and regression methods on ALSFW metric values and on estimates of forest structure attributes are analyzed. To do this, pulse density was randomly reduced and voxel parameters modified, obtaining ALSFW metric values for the different parameter combinations. These ALSFW metrics were used to estimate forest structure attributes with different regression methods. Secondly, a set of ALSFW data processing and analysis methods are integrated in a new software named WoLFeX (Waveform Lidar for Forestry eXtraction), including clipping, relative radiometric correction, voxelization and ALSFW metric extraction, and proposing new metrics for understory vegetation. Thirdly, the influence of the scan angle of ALS data acquisition and radiometric correction on the extraction of ALSFW metrics and on modeling forest fuel attributes is assessed. To do this, ALSFW metrics were extracted applying and without applying relative radiometric correction and using different scan angles. Fourthly, signal occlusion is characterized along the vertical forest structure using and comparing three different laser scanning configurations (ALSFW, ALSD and terrestrial laser scanning: TLS), determining their limitations in the detection of vegetative material in two contrasted forest ecosystems: boreal and Mediterranean. To quantify signal occlusion along the vertical forest structure, a new parameter based on the percentage of laser beams blocked prior to reach a given location, the rate of pulse reduction, is proposed. Fifthly, the assessment of how understory vegetation density classes are detected and determined by different ALS configurations is done. Vertical distribution profiles at the lower strata described by ALSFW and ALSD are compared with those described by TLS as reference. Moreover, understory vegetation density classes are determined by applying the Lorenz curve and Gini index from the vertical distribution profiles described by ALSFW and ALSD. Finally, the new proposed voxel-based ALSFW metrics are applied and evaluated, using TLS-based attributes as a reference, to estimate understory height, cover and volume in a Mediterranean ecosystem.[CA] Aquesta tesi aborda el desenvolupament de mètodes de processament i anàlisi de dades ALSFW per a la caracterització de l'estructura vertical del bosc i, en particular, del sotabosc. Per a respondre a aquest objectiu general, s'establiren sis objectius específics: En primer lloc, s'analitza la influència de la densitat de pols, dels paràmetres de voxelització (grandària de vóxel i valor d'assignació) i dels mètodes de regressió sobre els valors de les mètriques ALSFW i sobre l'estimació dels atributs d'estructura del bosc. Per a això, es reduí aleatòriament la densitat de polsos i es modificaren els paràmetres de voxelització, obtenint els valors de les mètriques ALSFW per a les diferents combinacions de paràmetres. Aquestes mètriques ALSFW s'empraren per a l'estimació d'atributs de l'estructura del bosc mitjançant diferents mètodes de regressió. En segon lloc, s'integraren mètodes de processament i d'anàlisi de dades ALSFW en una nova eina anomenada WoLFeX (Waveform Lidar for Forestry eXtraction) que inclou el processos de retallada, correcció radiomètrica relativa, voxelització i extracció de mètriques a partir de les dades ALSFW, així com noves mètriques descriptores del sotabosc. En tercer lloc, s'avalua la influència de l'angle de escaneig emprat en l'adquisició de les dades ALS i la correcció radiomètrica en l'extracció de mètriques ALSFW i en l'estimació d'atributs de combustibilitat forestal. Per a això, s'extragueren mètriques ALSFW amb i sense correcció radiomètrica relativa i emprant diferents angles d'escaneig. En quart lloc, es caracteritza l'oclusió del senyal al llarg de l'estructura vertical del bosc emprant i comparant tres tipus diferents de làser escàner (ALSFW, ALSD i làser escàner terrestre: TLS, per les seues sigles en anglès), determinant així les seues limitacions en la detecció de material vegetatiu en dos ecosistemes diferenciats: un boreal i un mediterrani. Per a quantificar l'oclusió del senyal al llarg de l'estructura vertical del bosc es proposa un nou paràmetre, la taxa de reducció del pols, basada en el percentatge de rajos làser bloquejats abans d'arribar a una posició donada. En cinquè lloc, s'avalua la manera en la qual es detecten i determinen les classes de densitat de sotabosc mitjançant els diferents tipus d'ALS. Es compararen els perfils de distribució vertical en estrats inferiors descrits per l'ALSFW i l'ALSD respecte als descrits pel TLS, emprant aquest últim com a referència. A més a més, es determinaren les classes de densitat de sotabosc aplicant la corba Lorenz i l'índex Gini a partir dels perfils de distribució vertical descrits per l'ALSFW i l'ALSD. Finalment, s'apliquen i avaluen les noves mètriques ALSFW basades en la voxelització, emprant com a referència els atributs extrets a partir del TLS, per a estimar l'alçada, la cobertura i el volum del sotabosc en un ecosistema mediterrani.Crespo Peremarch, P. (2020). Processing and analysis of airborne fullwaveform laser scanning data for the characterization of forest structure and fuel properties [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153715TESISCompendi

Vegetation Detection and Classification for Power Line Monitoring

Electrical network maintenance inspections must be regularly executed, to provide a continuous distribution of electricity. In forested countries, the electrical network is mostly located within the forest. For this reason, during these inspections, it is also necessary to assure that vegetation growing close to the power line does not potentially endanger it, provoking forest fires or power outages. Several remote sensing techniques have been studied in the last years to replace the labor-intensive and costly traditional approaches, be it field based or airborne surveillance. Besides the previously mentioned disadvantages, these approaches are also prone to error, since they are dependent of a human operator’s interpretation. In recent years, Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under debate, due to its flexibility and potential for customisation, as well as the fact it can fly close to the power lines. The present study proposes a vegetation management and power line monitoring method, using a UAV platform. This method starts with the collection of point cloud data in a forest environment composed of power line structures and vegetation growing close to it. Following this process, multiple steps are taken, including: detection of objects in the working environment; classification of said objects into their respective class labels using a feature-based classifier, either vegetation or power line structures; optimisation of the classification results using point cloud filtering or segmentation algorithms. The method is tested using both synthetic and real data of forested areas containing power line structures. The Overall Accuracy of the classification process is about 87% and 97-99% for synthetic and real data, respectively. After the optimisation process, these values were refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison and discussion of results is presented, providing the most important evaluation metrics and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso, durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede elétrica não a coloca em risco, provocando incêndios ou falhas elétricas. Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização, assim como o facto de conseguirem voar mais próximas das linhas elétricas. O presente estudo propõe um método para a gestão da vegetação e monitorização da rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo: deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas de classe através de um classificador baseado em features, vegetação ou estruturas da rede elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87% e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização, estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas de avaliação mais importantes e uma representação visual dos resultados obtidos