Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

International audienceNowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment

Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems

106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented

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

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

Belief Space-Guided Navigation for Robots and Autonomous Vehicles

Navigating through the environment is a fundamental capability for mobile robots, which is still very challenging today. Most robotic applications these days, such as mining, disaster response, and agriculture, require the robots to move and perform tasks in a variety of environments which are stochastic and sometimes even unpredictable. A robot often cannot directly observe its current state but instead estimates a distribution over the set of possible states based on sensor measurements that are both noisy and partial. The actual robot position differs from its prediction after applying a motion command, due to actuation noise. Classic algorithms for navigation should adapt themselves to where the behavior of the environment is stochastic, and the execution of the motions has great uncertainty. To solve such challenging problems, we propose to guide the robot's navigation in the belief space. Belief space-guided navigation differs fundamentally from planning without uncertainty where the state of the robot is always assumed to be known precisely. The robot senses its environment, estimates its current state due to perception uncertainty, and decides whether a new (or priori) action is appropriate. Based on that determination, it actuates its sensors to move with motion uncertainty in the environment. This inspires us to connect robot perception and motion planning, and reason about the uncertainty to improve the quality of plan so that the robot can follow a collision-free, feasible kinodynamic, and task-optimal trajectory. In this dissertation, we explore the belief space-guided robotic navigation problems, which include belief space-based scene understanding for autonomous vehicles and introduce belief space guided robotic planning. We first investigate how belief space can facilitate scene understanding under the context of lane marking quality assessment in the application of autonomous driving. We propose a new problem by measuring the quality of roads and ensuring they are ready for autonomous driving. We focus on developing three quality metrics for lane markings (LMs), correctness metric, shape metric, and visibility metric, and algorithms to assess LM qualities to facilitate scene understanding. As another example of using belief space for better scene understanding, we utilize crowdsourced images from multiple vehicles to help verify LMs for high-definition (HD) map maintenance. An LM is consistent if belief functions from the map and the image satisfy statistical hypothesis testing. We further extend the Bayesian belief model into a sequential belief update using crowdsourced images. LMs with a higher probability of existence are kept in the HD map whereas those with a lower probability of existence are removed from the HD map. Belief space can also help us to tightly connect perception and motion planning. As an example, we develop a motion planning strategy for autonomous vehicles. Named as virtual lane boundary approach, this framework considers obstacle avoidance, trajectory smoothness (to satisfy vehicle kinodynamic constraints), trajectory continuity (to avoid sudden movements), global positioning system (GPS) following quality (to execute the global plan), and lane following or partial direction following (to meet human expectation). Consequently, vehicle motion is more human-compatible than existing approaches. As another example of how belief space can help guide robots for different tasks, we propose to use it for the probabilistic boundary coverage of unknown target fields (UTFs). We employ Gaussian processes as a local belief function to approximate a field boundary distribution in an ellipse-shaped local region. The local belief function allows us to predict UTF boundary trends and establish an adjacent ellipse for further exploration. The process is governed by a depth-first search process until UTF is approximately enclosed by connected ellipses when the boundary coverage process ends. We formally prove that our boundary coverage process guarantees the enclosure above a given coverage ratio with a preset probability threshold