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

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15~months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.Comment: 9 page

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

Down-sampling of large lidar dataset in the context of off-road objects extraction

Nowadays, LiDAR (Light Detection and Ranging) is used in many fields, such as transportation. Thanks to the recent technological improvements, the current generation of LiDAR mapping instruments available on the market allows to acquire up to millions of three-dimensional (3D) points per second. On the one hand, such improvements allowed the development of LiDAR-based systems with increased productivity, enabling the quick acquisition of detailed 3D descriptions of the objects of interest. However, on the other hand, the extraction of the information of interest from such huge amount of acquired data can be quite challenging and time demanding. Motivated by such observation, this paper proposes the use of the Optimum Dataset method in order to ease and speed up the information extraction phase by significantly reducing the size of the acquired dataset while preserving (retain) the information of interest. This paper focuses on the data reduction of LiDAR datasets acquired on roads, with the goal of extraction the off-road objects. Mostly motivated by the need of mapping roads and quickly determining car position along a road, the development of efficient methods for the extraction of such kind of information is becoming a hot topic in the research community

Evaluation of automatically extracted landmarks for future driver assistance systems

In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by today's maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicle's position

Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds

Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles, and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research.Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Universidade de Vigo/CISU

Vehicle localization using landmarks obtained by a lidar mobile mapping system

Accurate and reliable localization in extensive outdoor environments will be a key ability of future driver assistance systems and autonomously driving vehicles. Relative localization, using sensors and a pre-mapped environment, will play a crucial role for such systems, because standard global navigation satellite system (GNSS) solutions will not be able to provide the required reliability. However, it is obvious that the environment maps will have to be quite detailed, making it a must to produce them fully automatically. In this paper, a relative localization approach is evaluated for an environment of substantial extent. The pre-mapped environment is obtained using a LIDAR mobile mapping van. From the raw data, landmarks are extracted fully automatically and inserted into a landmark map. Then, in a second campaign, a robotic vehicle is used to traverse the same scene. Landmarks are extracted from the sensor data of this vehicle as well. Using associated landmark pairs and an estimation approach, the positions of the robotic vehicle are obtained. The number of matches and the matching errors are analyzed, and it is shown that localization based on landmarks outperforms the vehicle's standard GNSS solution

Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices

Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data. The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment

16-01 Paths to ADA-Compliance: the Performance and Cost Efficiency of Measurement Technologies that Support ADA-Mandated, Self-Evaluations of Pedestrian Rights of Way

This study used terrestrial laser scanner and open source processing algorithms to develop an approach to automate the evaluation of transportation infrastructure in public rights of way. We estimated compliance or noncompliance of specific roadway features with the design standards adopted by the US Access Board and required under the Americans with Disabilities Act (ADA) such as minimum sidewalk width, maximum cross slopes and presence/absence of pedestrian connectivity automatically with extracting roadway features from point cloud data (PCD). We then compared the accuracy and cost efficiency of the automated with more conventional evaluative techniques to identify the potential risks, gains and the overall efficacy of these approaches. The collected raw data were processed through a sequential process including simplification, optimization, segmentation, and road feature categorization. Finally, the road elements were detected as the road feature objects. By developing a more thorough assessment process, this research provided communities with the information necessary to strategically plan transportation infrastructure improvements for people with limited mobility

Mapping of Road Facilities and Information on High Definition Maps using Mobile Mapping System

学位の種別: 修士University of Tokyo(東京大学