Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.
University of New South Wales（新南威尔士大学）；Agriculture Victoria（维多利亚州农业局）
Spatially and geometrically accurate laser scans are essential in modelling infrastructure for applications in civil, mining and transportation. Monitoring of underground or indoor environments such as mines or tunnels is challenging due to unavailability of a sensor positioning framework, complicated structurally symmetric layouts, repetitive features and occlusions. Current practices largely include a manual selection of discernable reference points for georeferencing and coregistration purpose. This study aims at overcoming these practical challenges in underground or indoor laser scanning.…
University of Waterloo（滑铁卢大学）；Chinese Academy of Sciences（中国科学院）；Jimei University（集美大学）
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentat…
Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds
Chair of Engineering Geodesy, TUM School of Engineering（工程大地测量学教授职位，技术大学慕尼黑工程与设计学院）；TUM Leonhard Obermeyer Center（技术大学慕尼黑Leonhard Obermeyer中心）；CV4DT, University of Cambridge（Cambridge大学CV4DT）
Transportation Research Institute, Old Dominion University（老多明尼大学交通研究所）
Detecting roadway segments inundated due to floodwater has important applications for vehicle routing and traffic management decisions. This paper proposes a set of algorithms to automatically detect floodwater that may be present in an image captured by mobile phones or other types of optical cameras. For this purpose, image classification and flood area segmentation methods are developed. For the classification task, we used Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and pre-trained deep neural network (VGG-16) as feature extractors and trained logistic regression, k-…
University of Waterloo（滑铁卢大学）；Central University of Finance and Economics（中央财经大学）
Stack interchanges are essential components of transportation systems. Mobile laser scanning (MLS) systems have been widely used in road infrastructure mapping, but accurate mapping of complicated multi-layer stack interchanges are still challenging. This study examined the point clouds collected by a new Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) system to perform the semantic segmentation task of a stack interchange. An end-to-end supervised 3D deep learning framework was proposed to classify the point clouds. The proposed method has proven to capture 3D features in co…
Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning
DGeo Research Group, Institute of Geography, Heidelberg University, Germany（地理研究所3DGeo研究组，海德堡大学）；Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany（跨学科科学计算中心（IWR），海德堡大学）
Southern University of Science and Technology（南方科技大学）
Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degree 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction. The novelty of this work is threefold: (1) developing a reprojection model from 3D points to 2D phase data towards phase registration with low computational com…
NormalView: tree species classification from backpack and aerial lidar data using geometric projections
Finnish Geospatial Research Institute FGI（芬兰地理研究 institute FGI）；The National Land Survey of Finland（芬兰国家土地调查局）；Department of Remote Sensing and Photogrammetry（遥感与摄影测量系）；Department of Built Environment, School of Engineering（环境系，工程学院）
With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist which, if proved effective can be exploited. We therefore argue that research i…
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
Norwegian Institute for Bioeconomy Research (NIBIO)（挪威生物经济研究所（NIBIO））；University College London（伦敦大学学院）；NERC National Centre for Earth Observation（NERC国家地球观测中心）
To help improve the safety and accessibility of indoor spaces, researchers and health professionals have created assessment instruments that enable homeowners and trained experts to audit and improve homes. With advances in computer vision, augmented reality (AR), and mobile sensors, new approaches are now possible. We introduce RASSAR (Room Accessibility and Safety Scanning in Augmented Reality), a new proof-of-concept prototype for semi-automatically identifying, categorizing, and localizing indoor accessibility and safety issues using LiDAR + camera data, machine learning, and AR. We presen…
The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the computational vision analysis method to record the residual quantity/fullness of the cabinet. To do so, it goes through a five-step method: object detection, foreground subtraction, K-means clustering, percentage estimation, and counting. The input image goes through the object detection method to analyze the specific position of the cabinets in terms of coordinates. After doing so, it goes through the foreground subtraction method to make the image more focus-able to the cabinet itself by removing the background (some manual wor…
The utilization of computational photography becomes increasingly essential in the medical field. Today, imaging techniques for dermatology range from two-dimensional (2D) color imagery with a mobile device to professional clinical imaging systems measuring additional detailed three-dimensional (3D) data. The latter are commonly expensive and not accessible to a broad audience. In this work, we propose a novel system and software framework that relies only on low-cost (and even mobile) commodity devices present in every household to measure detailed 3D information of the human skin with a 3D-g…
University of Oxford（牛津大学）；KTH Royal Institute of Technology（KTH皇家理工学院）；Scania AB（斯堪尼亚公司）
We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual feature…
Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color ground truth. This paper documents a multisensorial dataset named PolyU-BPCoMA which is distinctively positioned towards mobile colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted in…
The University of Sydney（悉尼大学）；The Australian Centre for Field Robotics（澳大利亚现场机器人中心）
Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system.…
University of Tartu（塔尔图大学）；Hasan Kalyoncu University（哈桑·卡利永朱大学）；Trinity College Dublin (TCD)（都柏林大学三一学院）
This paper provides an overview of 3D scanning methodologies and technologies proposed in the existing scientific and industrial literature. Throughout the paper, various types of the related techniques are reviewed, which consist, mainly, of close-range, aerial, structure-from-motion and terrestrial photogrammetry, and mobile, terrestrial and airborne laser scanning, as well as time-of-flight, structured-light and phase-comparison methods, along with comparative and combinational studies, the latter being intended to help make a clearer distinction on the relevance and reliability of the poss…
Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simulation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has tw…
Throughout the COVID-19 pandemic, the most common symptom displayed by patients has been a fever, leading to the use of temperature scanning as a preemptive measure to detect potential carriers of the virus. Human employees with handheld thermometers have been used to fulfill this task, however this puts them at risk as they cannot be physically distanced and the sequential nature of this method leads to great inconveniences and inefficiency. The proposed solution is an autonomously navigating robot capable of conversing and scanning people's temperature to detect fevers and help screen for CO…
Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset
Norwegian Institute for Bioeconomy Research (NIBIO)（挪威生物经济研究所）；University of Cambridge（剑桥大学）；Jan Evangelista Purkyně University in Ústí n. L.（拉贝河畔乌斯季扬·埃万杰利斯塔·普尔基涅大学）；University of Freiburg（弗莱堡大学）；Georg-August-Universität Göttingen（哥廷根大学）；TU Wien（维也纳工业大学）；Swiss Federal Institute for Forest, Snow and Landscape Research WSL（瑞士联邦森林、雪与景观研究所）；University of British Columbia（不列颠哥伦比亚大学）；Ghent University（根特大学）；Heidelberg University（海德堡大学）；University of Eastern Finland（东芬兰大学）；Silva Tarouca Research Institute（席尔瓦·塔劳卡研究所）；Forest Research Institute（森林研究所）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（联邦科学与工业研究组织）；Charles Darwin University（查尔斯·达尔文大学）；University College of London（伦敦大学学院）；Natural Resources Institute Finland (Luke)（芬兰自然资源研究所）；Council for Agricultural Research and Economics（农业研究与经济委员会）；University of Lethbridge（莱斯布里奇大学）；National Research Council – Institute of BioEconomy（国家研究委员会生物经济研究所）；Free University of Bolzano（博尔扎诺自由大学）
3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditions, and so on. Generative modeling of LiDAR data as scene priors is one of the promising solutions to compensate for unreliable or incomplete observations. In this paper, we propose a novel generative model for learning LiDAR data based on generative adversarial networks. As in the related studies, we process LiDAR data as a compact yet lossless representation, a cylindrical de…
