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

Classification and Change Detection in Mobile Mapping LiDAR Point Clouds

Creating 3D models of the static environment is an important task for the advancement of driver assistance systems and autonomous driving. In this work, a static reference map is created from a Mobile Mapping “light detection and ranging” (LiDAR) dataset. The data was obtained in 14 measurement runs from March to October 2017 in Hannover and consists in total of about 15 billion points. The point cloud data are first segmented by region growing and then processed by a random forest classification, which divides the segments into the five static classes (“facade”, “pole”, “fence”, “traffic sign”, and “vegetation”) and three dynamic classes (“vehicle”, “bicycle”, “person”) with an overall accuracy of 94%. All static objects are entered into a voxel grid, to compare different measurement epochs directly. In the next step, the classified voxels are combined with the result of a visibility analysis. Therefore, we use a ray tracing algorithm to detect traversed voxels and differentiate between empty space and occlusion. Each voxel is classified as suitable for the static reference map or not by its object class and its occupation state during different epochs. Thereby, we avoid to eliminate static voxels which were occluded in some of the measurement runs (e.g. parts of a building occluded by a tree). However, segments that are only temporarily present and connected to static objects, such as scaffolds or awnings on buildings, are not included in the reference map. Overall, the combination of the classification with the subsequent entry of the classes into a voxel grid provides good and useful results that can be updated by including new measurement data

Automatic segmentation and reconstruction of traffic accident scenarios from mobile laser scanning data

Virtual reconstruction of historic sites, planning of restorations and attachments of new building parts, as well as forest inventory are few examples of fields that benefit from the application of 3D surveying data. Originally using 2D photo based documentation and manual distance measurements, the 3D information obtained from multi camera and laser scanning systems realizes a noticeable improvement regarding the surveying times and the amount of generated 3D information. The 3D data allows a detailed post processing and better visualization of all relevant spatial information. Yet, for the extraction of the required information from the raw scan data and for the generation of useable visual output, time-consuming, complex user-based data processing is still required, using the commercially available 3D software tools. In this context, the automatic object recognition from 3D point cloud and depth data has been discussed in many different works. The developed tools and methods however, usually only focus on a certain kind of object or the detection of learned invariant surface shapes. Although the resulting methods are applicable for certain practices of data segmentation, they are not necessarily suitable for arbitrary tasks due to the varying requirements of the different fields of research. This thesis presents a more widespread solution for automatic scene reconstruction from 3D point clouds, targeting street scenarios, specifically for the task of traffic accident scene analysis and documentation. The data, obtained by sampling the scene using a mobile scanning system is evaluated, segmented, and finally used to generate detailed 3D information of the scanned environment. To realize this aim, this work adapts and validates various existing approaches on laser scan segmentation regarding the application on accident relevant scene information, including road surfaces and markings, vehicles, walls, trees and other salient objects. The approaches are therefore evaluated regarding their suitability and limitations for the given tasks, as well as for possibilities concerning the combined application together with other procedures. The obtained knowledge is used for the development of new algorithms and procedures to allow a satisfying segmentation and reconstruction of the scene, corresponding to the available sampling densities and precisions. Besides the segmentation of the point cloud data, this thesis presents different visualization and reconstruction methods to achieve a wider range of possible applications of the developed system for data export and utilization in different third party software tools

Automatic Generation of Urban Road 3D Models for Pedestrian Studies From LiDAR Data

[Abstract] The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research.This research study was funded by the Directorate-General for Traffic of Spain, grant number SPIP2017-0234

Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

International audienceWe propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature

Reconstruction of 3D Urban Scenes Using a Moving Lidar Sensor

In this report, we propose algorithms which interpret and display 3D environments.The input of this procedure is a LiDAR sensor mounted atop of a car. The sensor outputs a data stream covering more than 100 meters radius of space, collecting data at 15Hz. The recording is done in real environment on the streets of Budapest in real time, while the processing is offline, implemented on CPU keeping in mind the future implementation on GPUs to reach real time data processing. The aim is to segment several region classes (such as roads, building walls, vegetation) and to identify specified objects (such as people, vehicles, traffic signs) in the point clouds through a presegmentation step. To achieve this classification, we need several features such as the color and geometrical properties of the specified objects and their possible geometrical and physical interactions. Also, we need to take into account the time domain features calculated based on the LiDAR data stream. After this presegmentation step we are able to reconstruct building facades in 3D and to track the detected objects in the 3D space. Also, in the future, this processed data set can be registered against 2D images provided by conventional cameras to reproduce realistic, colored 3D virtua