PATHWAY DETECTION AND GEOMETRICAL DESCRIPTION FROM ALS DATA IN FORESTED MOUNTANEOUS AREA

International audienceIn the last decade, airborne laser scanning (ALS) systems have become an alternative source for the acquisition of altimeter data. Compared to high resolution orthoimages, one of the main advantages of ALS is the ability of the laser beam to penetrate vegetation and reach the ground underneath. Therefore, 3D point clouds are essential data for computing Digital Terrain Models (DTM) in natural and vegetated areas. DTMs are a key product for many applications such as tree detection, flood modelling, archeology or road detection. Indeed, in forested areas, traditional image-based algorithms for road and pathway detection would partially fail due to their occlusion by the canopy cover. Thus, crucial information for forest management and fire prevention such as road width and slope would be misevaluated. This paper deals with road and pathway detection in a complex forested mountaneous area and with their geometrical parameter extraction using lidar data. Firstly, a three-step image-based methodology is proposed to detect road regions. Lidar feature orthoimages are first generated. Then, road seeds are both automatically and semi-automatically detected. And, a region growing algorithm is carried out to retrieve the full pathways from the seeds previously detected. Secondly, these pathways are vectorized using morphological tools, smoothed, and discretized. Finally, 1D sections within the lidar point cloud are successively generated for each point of the pathways to estimate more accurately road widths in 3D. We also retrieve a precise location of the pathway borders and centers, exported as vector data

ROAD NETWORK IDENTIFICATION AND EXTRACTION IN SATELLITE IMAGERY USING OTSU'S METHOD AND CONNECTED COMPONENT ANALYSIS

As the high resolution satellite images have become easily available, this has motivated researchers for searching advanced methods for object detection and extraction from satellite images. Roads are important curvilinear object as they are a used in urban planning, emergency response, route planning etc. Automatic road detection from satellite images has now become an important topic in photogrammetry with the advances in remote sensing technology. In this paper, a method for road detection and extraction of satellite images has been introduced. This method uses the concept of histogram equalization, Otsu's method of image segmentation, connected component analysis and morphological operations. The aim of this paper is to discover the potential of high resolution satellite images for detecting and extracting the road network in a robust manner

Dense Point Cloud Extraction From Oblique Imagery

With the increasing availability of low-cost digital cameras with small or medium sized sensors, more and more airborne images are available with high resolution, which enhances the possibility in establishing three dimensional models for urban areas. The high accuracy of representation of buildings in urban areas is required for asset valuation or disaster recovery. Many automatic methods for modeling and reconstruction are applied to aerial images together with Light Detection and Ranging (LiDAR) data. If LiDAR data are not provided, manual steps must be applied, which results in semi-automated technique. The automated extraction of 3D urban models can be aided by the automatic extraction of dense point clouds. The more dense the point clouds, the easier the modeling and the higher the accuracy. Also oblique aerial imagery provides more facade information than nadir images, such as building height and texture. So a method for automatic dense point cloud extraction from oblique images is desired. In this thesis, a modified workflow for the automated extraction of dense point clouds from oblique images is proposed and tested. The result reveals that this modified workflow works well and a very dense point cloud can be extracted from only two oblique images with slightly higher accuracy in flat areas than the one extracted by the original workflow. The original workflow was established by previous research at the Rochester Institute of Technology (RIT) for point cloud extraction from nadir images. For oblique images, a first modification is proposed in the feature detection part by replacing the Scale-Invariant Feature Transform (SIFT) algorithm with the Affine Scale-Invariant Feature Transform (ASIFT) algorithm. After that, in order to realize a very dense point cloud, the Semi-Global Matching (SGM) algorithm is implemented in the second modification to compute the disparity map from a stereo image pair, which can then be used to reproject pixels back to a point cloud. A noise removal step is added in the third modification. The point cloud from the modified workflow is much denser compared to the result from the original workflow. An accuracy assessment is made in the end to evaluate the point cloud extracted from the modified workflow. From the two flat areas, subsets of points are selected from both original and modified workflow, and then planes are fitted to them, respectively. The Mean Squared Error (MSE) of the points to the fitted plane is compared. The point subsets from the modified workflow have slightly lower MSEs than the ones from the original workflow, respectively. This suggests a much more dense and more accurate point cloud can lead to clear roof borders for roof extraction and improve the possibility of 3D feature detection for 3D point cloud registration

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

Semi-automatic Road Extraction from Very High Resolution Remote Sensing Imagery by RoadModeler

Accurate and up-to-date road information is essential for both effective urban planning and disaster management. Today, very high resolution (VHR) imagery acquired by airborne and spaceborne imaging sensors is the primary source for the acquisition of spatial information of increasingly growing road networks. Given the increased availability of the aerial and satellite images, it is necessary to develop computer-aided techniques to improve the efficiency and reduce the cost of road extraction tasks. Therefore, automation of image-based road extraction is a very active research topic. This thesis deals with the development and implementation aspects of a semi-automatic road extraction strategy, which includes two key approaches: multidirectional and single-direction road extraction. It requires a human operator to initialize a seed circle on a road and specify a extraction approach before the road is extracted by automatic algorithms using multiple vision cues. The multidirectional approach is used to detect roads with different materials, widths, intersection shapes, and degrees of noise, but sometimes it also interprets parking lots as road areas. Different from the multidirectional approach, the single-direction approach can detect roads with few mistakes, but each seed circle can only be used to detect one road. In accordance with this strategy, a RoadModeler prototype was developed. Both aerial and GeoEye-1 satellite images of seven different types of scenes with various road shapes in rural, downtown, and residential areas were used to evaluate the performance of the RoadModeler. The experimental results demonstrated that the RoadModeler is reliable and easy-to-use by a non-expert operator. Therefore, the RoadModeler is much better than the object-oriented classification. Its average road completeness, correctness, and quality achieved 94%, 97%, and 94%, respectively. These results are higher than those of Hu et al. (2007), which are 91%, 90%, and 85%, respectively. The successful development of the RoadModeler suggests that the integration of multiple vision cues potentially offers a solution to simple and fast acquisition of road information. Recommendations are given for further research to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

무인자율주행을 위한 도로 지도 생성 및 측위

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 서승우.This dissertation aims to present precise and cost-efficient mapping and localization algorithms for autonomous vehicles. Mapping and localization are ones of the key components in autonomous vehicles. The major concern for mapping and localization research is maximizing the accuracy and precision of the systems while minimizing the cost. For this goal, this dissertation proposes a road map generation system to create a precise and efficient lane-level road map, and a localization system based on the proposed road map and affordable sensors. In chapter 2, the road map generation system is presented. The road map generation system integrates a 3D LIDAR data and high-precision vehicle positioning system to acquire accurate road geometry data. Acquired road geometry data is represented as sets of piecewise polynomial curves in order to increase the storage efficiency and the usability. From extensive experiments using a real urban and highway road data, it is verified that the proposed road map generation system generates a road map that is accurate and more efficient than previous road maps in terms of the storage efficiency and usability. In chapter 3, the localization system is presented. The localization system targets an environment that the localization is difficult due to the lack of feature information for localization. The proposed system integrates the lane-level road map presented in chapter 2, and various low-cost sensors for accurate and cost-effective vehicle localization. A measurement ambiguity problem due to the use of low-cost sensors and poor feature information was presented, and a probabilistic measurement association-based particle filter is proposed to resolve the measurement ambiguity problem. Experimental results using a real highway road data is presented to verify the accuracy and reliability of the localization system. In chapter 4, an application of the accurate vehicle localization system is presented. It is demonstrated that sharing of accurate position information among vehicles can improve the traffic flow and suppress the traffic jam effectively. The effect of the position information sharing is evaluated based on numerical experiments. For this, a traffic model is proposed by extending conventional SOV traffic model. The numerical experiments show that the traffic flow is increased based on accurate vehicle localization and information sharing among vehicles.Chapter 1 Introduction 1 1.1 Background andMotivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Generation of a Precise and Efficient Lane-Level Road Map 3 1.2.2 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 4 1.2.3 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 4 Chapter 2 Generation of a Precise and Efficient Lane-Level Road Map 6 2.1 RelatedWorks 9 2.1.1 Acquisition of Road Geometry 11 2.1.2 Modeling of Road Geometry 13 2.2 Overall System Architecture 15 2.3 Road Geometry Data Acquisition and Processing 17 2.3.1 Data Acquisition 18 2.3.2 Data Processing 18 2.3.3 Outlier Problem 26 2.4 RoadModeling 27 2.4.1 Overview of the sequential approximation algorithm 29 2.4.2 Approximation Process 30 2.4.3 Curve Transition 35 2.4.4 Arc length parameterization 38 2.5 Experimental Validation 39 2.5.1 Experimental Setup 39 2.5.2 Data Acquisition and Processing 40 2.5.3 RoadModeling 42 2.6 Summary 49 Chapter 3 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 51 3.1 RelatedWorks 53 3.2 SystemOverview 57 3.2.1 Test Vehicle and Sensor Configuration 57 3.2.2 Augmented RoadMap Data 57 3.2.3 Vehicle Localization SystemArchitecture 61 3.2.4 ProblemStatement 62 3.3 Particle filter-based Vehicle Localization Algorithm 63 3.3.1 Initialization 65 3.3.2 Time Update 66 3.3.3 Measurement Update 66 3.3.4 Integration 68 3.3.5 State Estimation 68 3.3.6 Resampling 69 3.4 Map-Image Measurement Update with Probabilistic Data Association 69 3.4.1 Lane Marking Extraction and Measurement Error Model 70 3.5 Experimental Validation 76 3.5.1 Experimental Environments 76 3.5.2 Localization Accuracy 77 3.5.3 Effect of the Probabilistic Measurement Association 79 3.5.4 Effect of theMeasurement ErrorModel 80 3.6 Summary 80 Chapter 4 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 82 4.1 Extended SOVModel 84 4.1.1 SOVModel 85 4.1.2 Extended SOVModel 89 4.2 Results and Discussions 91 4.3 Summary 93 Chapter 5 Conclusion 95 Bibliography 97 국문 초록 108Docto

Simultane Lokalisierung und Kartierung spurgeführter Systeme

Voraussetzung für eine zuverlässige Lokalisierung im Schienenverkehr sind geometrische Karten des Trassennetzes. Diese existieren häufig nicht. Manuelle Verfahren zur Kartierung sind mit hohen Kosten verbunden. Abhilfe schafft die Verwendung fahrzeuginterner Sensoren. Deren automatisierte Verarbeitung erfordert simultan zur Kartierung der Umgebung die Lokalisierung des Fahrzeugs. Abhängigkeiten zwischen der Fahrzeugbewegung und der Karte werden auf diese Weise berücksichtigt

Simultane Lokalisierung und Kartierung spurgeführter Systeme

Voraussetzung für eine zuverlässige Lokalisierung im Schienenverkehr sind geometrische Karten des Trassennetzes. Diese existieren häufig nicht. Manuelle Verfahren zur Kartierung sind mit hohen Kosten verbunden. Abhilfe schafft die Verwendung fahrzeuginterner Sensoren. Deren automatisierte Verarbeitung erfordert simultan zur Kartierung der Umgebung die Lokalisierung des Fahrzeugs. Abhängigkeiten zwischen der Fahrzeugbewegung und der Karte werden auf diese Weise berücksichtigt

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas