Mobile terrestrial lidar data to detect traffic sign and light pole/ Dados de laser terrestre móvel para detectar placas de sinalização e postes de iluminação

This paper introduces a method for detecting and classifying vertical objects from a mobile terrestrial laser scanner point cloud. The paper concentrates on the classification of the top of the poles, where shields or lamps are installed. First, the variance-covariance matrix of each segmented object is computed. Then the eigenvalues and eigenvector of this matrix are derived. The 3D coordinates of each point are then transformed using the principal components transform in order to compute new features in this new space. In the second step the distribution of the three eigenvalues of the different classes in the eigenvalues space is analysed. Is it deduced that similar objects align in this space, allowing proposing a classification rule based on the distance to the lines. An experiment was performed to verify the approach performance. In the classification of different objects, the global accuracy reached 75%. When the classification was more general, separating just flat from three-dimensional objects the accuracy reached 94%. From the obtained results it can be concluded that the proposed method is feasible and allows separating objects according to its shap

Optimizing Bus Stop Environments: Analysis of Sun Glare Reduction with Green Elements in MLS and GPR Data

Urban transportation is pivotal in facilitating citizen mobility, and within this sphere, the thermal comfort experienced at bus stops significantly impacts pedestrians' well-being. This research delves into the analysis of direct sun glare at bus stops utilizing Mobile Laser Scanning (MLS) point clouds. Solar angles are calculated to evaluate sun rays, focusing particularly on the summer solstice, and considering the integration of trees to alleviate direct sun glare. The methodological approach comprises four stages: 1) Semantic segmentation of the bus stop environment, 2) Sun glare analysis, 3) Sun glare analysis integrating trees, and 4) Subsoil investigation with Ground-Penetrating Radar (GPR). Various algorithms, including DBSCAN and filters for height, width, and intensity, are employed to classify different elements contained in the bus stop. Sun rays are then derived based on sun angles, identifying intersections with street elements and vertical structures like bus stops or facades. Subsequently, these intersections are recalculated, incorporating trees in the bus stop vicinity. Finally, GPR is utilized to assess the viability of adding trees. Three bus stops in Vigo, Spain, serve as the focal points of analysis. The study delineates specific time intervals when sun rays either encounter new vegetation obstacles or remain unobstructed. While both small and large trees show potential in reducing sun glare, larger trees notably exhibit greater effectiveness in blocking sun glare for extended periods

Automatic manhole extraction from MMS data to update basemaps

Basemaps are the main resource used in urban planning, building and infrastructure asset management. Therefore, they must be accurate and up to date to better serve citizens, contractors, property owners and town planning departments. Traditionally, they have been updated by aerial photogrammetry, but this is not always possible and alternatives need to be sought. In such cases, a useful option for large scales is the mobile mapping system (MMS). However, automatic extraction from MMS point clouds is limited by the complexity of the urban environment. Therefore, the influence of the urban pattern is analysed in three zones with varied urban characteristics: areas with high buildings, open areas, and areas with a low level of urbanization. In these areas, the capture and automatic extraction of 3D urban elements is performed using commercial software, which is useful for some elements but not for manholes. The objective of this study is to establish a methodology for extracting manholes automatically and completing hidden buildings' corners, in order to update urban basemaps. Shape and intensity are the main detection parameters for manholes, whereas additional information from satellite image Quickbird is used to complete the buildings. The worst rate of detection for all the extracted urban elements was found in areas of high buildings. Finally, the article analyses the computing cost for manhole extraction, and the economic cost and time consume of the entire process, including the proposed methodolgy using an MMS point cloud and the traditional survey in this case.Peer ReviewedPostprint (updated version

Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds

Automatic curb detection is an important issue in road maintenance, three-dimensional (3D) urban modeling, and autonomous navigation fields. This paper is focused on the segmentation of curbs and street boundaries using a 3D point cloud captured by a mobile laser scanner (MLS) system. Our method provides a solution based on the projection of the measured point cloud on the XY plane. Over that plane, a segmentation algorithm is carried out based on morphological operations to determine the location of street boundaries. In addition, a solution to extract curb edges based on the roughness of the point cloud is proposed. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. The proposed method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. The extraction method provides completeness and correctness rates above 90% and quality values higher than 85% in both studied datasets.Ministerio de Ciencia e Innovació

A low-cost collaborative location scheme with GNSS and RFID for the Internet of Things

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities

Vehicle global 6-DoF pose estimation under traffic surveillance camera

Abstract(#br)Accurately sensing the global position and posture of vehicles in traffic surveillance videos is a challenging but valuable issue for future intelligent transportation systems. Although in recent years, deep learning has brought about major breakthroughs in the six degrees of freedom (6-DoF) pose estimation of objects from monocular images, accurate estimation of the geographic 6-DoF poses of vehicles using images from traffic surveillance cameras remains challenging. We present an architecture that computes continuous global 6-DoF poses throughout joint 2D landmark estimation and 3D pose reconstruction. The architecture infers the 6-DoF pose of a vehicle from the appearance of the image of the vehicle and 3D information. The architecture, which does not rely on intrinsic camera parameters, can be applied to all surveillance cameras by a pre-trained model. Also, with the help of 3D information from the point clouds and the 3D model itself, the architecture can predict landmarks with few and/or blurred textures. Moreover, because of the lack of public training datasets, we release a large-scale dataset, ADFSC, that contains 120 K groups of data with random viewing angles. Regarding both 2D and 3D metrics, our architecture outperforms existing state-of-the-art algorithms in vehicle 6-DoF estimation

Update urban basemap by using the LiDAR mobile mapping system : the case of Abu Dhabi municipal system

Basemaps are the main resource used in urban planning and in building and infrastructure asset management. These maps are used by citizens and by private and public stakeholders. Therefore, accurate, up-to-date geoinformation of reference are needed to provide a good service. In general, basemaps have been updated by aerial photogrammetry or field surveying, but these methods are not always possible and alternatives need to be sought. Current limitations and challenges that face traditional field surveys include areas with extreme weather, deserts or artic environments, and flight restrictions due to proximity with other countries if there is not an agreement. In such cases, alternatives for large-scale are required. This thesis proposes the use of a mobile mapping system (MMS) to update urban basemaps. Most urban features can be extracted from point cloud using commercial software or open libraries. However, there are some exceptions: manhole covers, or hidden elements even with captures from defferent perspective, the most common building corners. Therefore, the main objective of this study was to establish a methodology for extracting manholes automatically and for completing hidden corners of buildings, so that urban basemaps can be updated. The algorithm developed to extract manholes is based on time, intensity and shape detection parameters, whereas additional information from satellite images is used to complete buildings. Each municipality knows the materials and dimensions of its manholes. Taking advantage of this knowledge, the point cloud is filtered to classify points according to the set of intensity values associated with the manhole material. From the classified points, the minimum bounding rectangles (MBR) are obtained and finally the shape is adjusted and drawn. We use satellite imagery to automatically digitize the layout of building footprints with automated software tools. Then, the visible corners of buildings from the LiDAR point cloud are imported and a fitting process is performed by comparing them with the corners of the building from the satellite image. Two methods are evaluated to establish which is the most suitable for adjustment in these conditions. In the first method, the differences in X and Y directions are measured in the corners, where LiDAR and satellite data are available, and is often computed as the average of the offsets. In the second method, a Helmert 2D transformation is applied. MMS involves Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU) to georeference point clouds. Their accuracy depends on the acquisition environment. In this study, the influence of the urban pattern is analysed in three zones with varied urban characteristics: different height buildings, open areas, and areas with a low and high level of urbanization. To evaluate the efficiency of the proposed algorithms, three areas were chosen with varying urban patterns in Abu Dhabi. In these areas, 3D urban elements (light poles, street signs, etc) were automatically extracted using commercial software. The proposed algorithms were applied to the manholes and buildings. The completeness and correctness ratio, and geometric accuracy were calculated for all urban elements in the three areas. The best success rates (>70%) were for light poles, street signs and road curbs, regardless of the height of the buildings. The worst rate was obtained for the same features in peri-urban areas, due to high vegetation. In contrast, the best results for trees were found in theses areas. Our methodology demonstrates the great potential and efficiency of mobile LiDAR technology in updating basemaps; a process that is required to achieve standard accuracy in large scale maps. The cost of the entire process and the time required for the proposed methodology was calculated and compared with the traditional method. It was found that mobile LiDAR could be a standard cost-efficient procedure for updating maps.La cartografía de referencia es la principal herramienta en planificación urbanística, y gestión de infraestructuras y edificios, al servicio de ciudadanos, empresas y administración. Por esta razón, debe estar actualizada y ser lo más precisa posible. Tradicionalmente, la cartografía se actualiza mediante fotogrametría aérea o levantamientos terrestres. No obstante, deben buscarse alternativas válidas para escalas grandes, porque no siempre es posible emplear estas técnicas debido a las limitaciones y retos actuales a los que se enfrenta la medición tradicional en algunas zonas del planeta, con meteorología extrema o restricciones de vuelo por la proximidad a la frontera con otros países. Esta tesis propone el uso del sistema Mobile Mapping System (MMS) para actualizar la cartografía urbana de referencia. La mayoría de los elementos pueden extraerse empleando software comercial o librerías abiertas, excepto los registros de servicios. Los elementos ocultos son otro de los inconvenientes encontrados en el proceso de creación o actualización de la cartografía, incluso si se dispone de capturas desde diferentes puntos de vista. El caso más común es el de las esquinas de edificios. Por ello, el principal objetivo de este estudio es establecer una metodología de extracción automática de los registros y completar las esquinas ocultas de los edificios para actualizar cartografía urbana. El algoritmo desarrollado para la detección y extracción de registros se basa en parámetros como el tiempo, la intensidad de la señal laser y la forma de los registros, mientras que para completar los edificios se emplea información adicional de imágenes satélite. Aprovechando el conocimiento del material y dimensión de los registros, en disposición de los gestores municipales, el algoritmo propuesto filtra y clasifica los puntos de acuerdo a los valores de intensidad. De aquellos clasificados como registros se calcula el mínimo rectángulo que los contiene (Minimum Bounding Rectangle) y finalmente se ajusta la forma y se dibuja. Las imágenes de satélite son empleadas para obtener automáticamente la huella de los edificios. Posteriormente, se importan las esquinas visibles de los edificios obtenidas desde la nube de puntos y se realiza el ajuste comparándolas con las obtenidas desde satélite. Para llevar a cabo este ajuste se han evaluado dos métodos, el primero de ellos considera las diferencias entre las coordenadas XY, desplazándose el promedio. En el segundo, se aplica una transformación Helmert2D. MMS emplea sistemas de navegación global por satélite (Global Navigation Satellite Systems, GNSS) e inerciales (Inertial Measurement Unit, IMU) para georreferenciar la nube de puntos. La precisión de estos sistemas de posicionamiento depende del entorno de adquisición. Por ello, en este estudio se han seleccionado tres áreas con distintas características urbanas (altura de edificios, nivel de urbanización y áreas abiertas) de Abu Dhabi con el fin de analizar su influencia, tanto en la captura, como en la extracción de los elementos. En el caso de farolas, señales viales, árboles y aceras se ha realizado con software comercial, y para registros y edificios con los algoritmos propuestos. Las ratios de corrección y completitud, y la precisión geométrica se han calculado en las diferentes áreas urbanas. Los mejores resultados se han conseguido para las farolas, señales y bordillos, independientemente de la altura de los edificios. La peor ratio se obtuvo para los mismos elementos en áreas peri-urbanas, debido a la vegetación. Resultados opuestos se han conseguido en la detección de árboles. El coste económico y en tiempo de la metodología propuesta resulta inferior al de métodos tradicionales. Lo cual demuestra el gran potencial y eficiencia de la tecnología LiDAR móvil para la actualización cartografía de referenciaPostprint (published version

An Efficient Point Cloud Processing Framework for Terrestrial and Mobile Lidar Data via Reconstructing the Scan Pattern Grid

Lidar (LIght Detection And Ranging) is a remote sensing technology using light in the form of a pulsed laser, which enables efficient, accurate, 3-D data acquisition of a scene. Depending on the mounting platform, lidar data acquisition can be categorized into Airborne Laser Scanning (ALS, or airborne lidar), Terrestrial Laser Scanning (TLS, or terrestrial lidar), and Mobile Laser Scanning (MLS, or mobile lidar). The lidar technique has been widely used for a plethora of applications including topographic mapping, bathymetric mapping, utility mapping, engineering surveying, agriculture, forestry, geology, architecture, industrial facilities, cultural heritage, asset management, construction, and so forth. However, efficiently processing the dense datasets produced by lidar still remains challenging given the large data volume. In addition, because of the scan pattern, range, view angle, and other factors, the point density for terrestrial and mobile lidar data can vary dramatically across the scene, which raises different challenges in developing robust processing methods compared with an ALS point cloud, which tends to be more evenly distributed. To overcome the challenges in processing TLS and MLS data, in this research, the point cloud is structured into a 2-D grid structure called the scan pattern grid, which represents the way that a scanner collects data. This dissertation, comprising four manuscripts, explores the possibilities and performance improvements of exploiting this scan pattern grid to process point cloud data. This first manuscript presents an efficient ground filtering method for TLS data via a Scanline Density Analysis. Ground filtering is a common procedure in lidar data processing, which separates the point cloud data into two classes: ground points and non-ground points. The proposed process first separates the ground candidates, density features, and unidentified points based on an analysis of point density within each scanline. Second, a region growth using the scan pattern clusters the ground candidates using the density features as boundaries and further refines the ground points. Both stages process and analyze the TLS data in each scan separately, exploiting the scan pattern grid for efficiency. The next two manuscripts develop a novel point cloud segmentation with an approach that links the scan pattern grids from multiple scans during the analysis. Point cloud segmentation groups points with similar attributes with respect to geometric, colormetric radiometric, and/or other information to help with object extraction and interpreting the point cloud. The proposed segmentation method only requires the basic geometric information and consists of two main steps. First, a novel feature extraction approach, NORmal VAriation ANAlysis (Norvana), eliminates some noise points and extracts edge points without requiring a general (and error prone) normal estimation at each point. Second, region growing groups the points on a smooth surface using the edge points as boundaries to obtain the segmentation result. Unlike TLS data that can be directly structured from a structured format (e.g., ASTM E57), Mobile lidar data is usually stored in an unorganized manner (e.g., ASPRS LAS). The final manuscript presents an efficient mobile lidar data processing framework including an approach to reconstruct the scanner trajectory such that an unorganized point cloud can be structured into the scan pattern grid based on the order of acquisition and revolutions of the scanner. Then the concept of Norvana for edge detection, normal estimation, feature extraction, and segmentation, is extended to be suitable for processing mobile lidar data and is named Mo norvana. Additionally, the proposed framework also introduces an efficient data visualization scheme exploiting the scan pattern grid. All of the proposed methods implement parallel processing to obtain a higher computational performance. The effectiveness, efficiency, robustness, and versatility are demonstrated both qualitatively and quantitatively by testing multiple terrestrial and mobile lidar datasets collected by different scanners with different spatial scales, resolutions, and scene types. The key contribution of this research is a generalized point cloud processing framework that can efficiently support a wide range of refinements, processes, and analysis for a variety of applications