Implementation of a hybrid localization system applied to the autonomous navigation of land vehicles

Orientador: Janito Vaqueiro FerreiraDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: A pesquisa de veículos autônomos vem se intensificando nos últimos anos. O principal objetivo dessa área é a condução segura e a redução de acidentes. No entanto, o alto custo dos veículos autônomos atuais ainda é uma grande barreira para a disseminação de seu uso. Visando atingir esse objetivo, trabalhos vem sendo desenvolvidos com a finalidade de reduzir o custo e aumentar a robustez e a eficiência. Considerando esses objetivos, esta pesquisa propõe um sistema de localização híbrido em ambiente simulado, para a fusão dos dados de sensores GPS, um sensor de bússola e também a saída de uma implementação de um Método de Localização Referenciado (MLR) processando sinais de um LIDAR. O método consiste inicialmente em utilizar um sistema de percepção com câmera e um sensor de distância para detectar objetos conhecidos do ambiente e consultar as suas respectivas coordenadas numa base de dados geográficos com o objetivo de assim estimar a localização do veículo. Finalmente, a implementação do filtro de Kalman para fundir os dados do MLR e dos sensores GPS e bússola. Para avaliar o desempenho do método, foi desenvolvida uma plataforma de simulação no ambiente CARLA com os dados dos sensores acessados pelo ROS. Todo o sistema simulado é executado em tempo real e seus resultados são muito consistentes com o ambiente realAbstract: Autonomous vehicle research has intensifyed in the recent years. The main objective of this area is safe driving and accident reduction. However, the high cost of current autonomous vehicles is still a major barrier to its disseminated use. In order to achieve these goals, research has been targeting to reduce cost and increase robustness and efficiency. Considering these objectives, this work proposes a hybrid localization system in a simulated environment, for the sensor fusion of GPS, a compass sensor and also the output of an implementation of a Referenced Location Method (RLM) processing LIDAR data. The method consists initially of using a perception system with a camera and a distance sensor, to detect known objects from the environment, and query the respective coordinates from a geographic database, in order to estimate the respective vehicle position. Finally, the implementation of a Kalman filter to fuse data from the RLM and the GPS and compass sensors. To assess the method performance, a simulation platform was developed in the CARLA environment with the data of the sensors accessed by ROS. The whole simulated system is executed in real time and its results are very consistent to a real environmentMestradoMecânica de Sólidos e Projeto MecânicoMestre em Engenharia Mecânic

Context-aware GPS Integrity Monitoring for Intelligent Transport Systems (ITS)

The integrity of positioning systems has become an increasingly important requirement for location-based Intelligent Transports Systems (ITS). The navigation systems, such as Global Positioning System (GPS), used in ITS cannot provide the high quality positioning information required by most services, due to the various type of errors from GPS sensor, such as signal outage, and atmospheric effects, all of which are difficult to measure, or from the map matching process. Consequently, an error in the positioning information or map matching process may lead to inaccurate determination of a vehicle’s location. Thus, the integrity is require when measuring both vehicle’s positioning and other related information such as speed, to locate the vehicle in the correct road segment, and avoid errors. The integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information; as this may lead to a significant error arising in the ITS services. Hence, to achieve the integrity requirement a navigation system should have a robust mechanism, to notify the user of any potential errors in the navigation information. The main aim of this research is to develop a robust and reliable mechanism to support the positioning requirement of ITS services. This can be achieved by developing a high integrity GPS monitoring algorithm with the consideration of speed, based on the concept of context-awareness which can be applied with real time ITS services to adapt changes in the integrity status of the navigation system. Context-aware architecture is designed to collect contextual information about the vehicle, including location, speed and heading, reasoning about its integrity and reactions based on the information acquired. In this research, three phases of integrity checks are developed. These are, (i) positioning integrity, (ii) speed integrity, and (iii) map matching integrity. Each phase uses different techniques to examine the consistency of the GPS information. A receiver autonomous integrity monitoring (RAIM) algorithm is used to measure the quality of the GPS positioning data. GPS Doppler information is used to check the integrity of vehicle’s speed, adding a new layer of integrity and improving the performance of the map matching process. The final phase in the integrity algorithm is intended to verify the integrity of the map matching process. In this phase, fuzzy logic is also used to measure the integrity level, which guarantees the validity and integrity of the map matching results. This algorithm is implemented successfully, examined using real field data. In addition, a true reference vehicle is used to determine the reliability and validity of the output. The results show that the new integrity algorithm has the capability to support a various types of location-based ITS services.Saudi Arabia Cultural Burea

Enhanced indoor positioning utilising wi-fi fingerprint and QR calibration techniques

The growing interest in location-based services (LBS), due to the demand for its application in personal navigation, billing and information enquiries, has expedited the research development for indoor positioning techniques. The widely used global positioning system (GPS) is a proven technology for positioning, navigation, but it performs poorly indoors. Hence, researchers seek alternative solutions, including the concept of signal of opportunity (SoOP) for indoor positioning. This research planned to use cheap solutions by utilizing available communication system infrastructure without the need to deploy new transmitters or beacons for positioning purposes. Wi-Fi fingerprinting has been identified for potential indoor positioning due to its availability in most buildings. In unplanned building conditions where the available number of APs is limited and the locations of APs are predesignated, certain positioning algorithms do not perform well consistently. In addition, there are several other factors that influence positioning accuracy, such as different path movements of users and different Wi-Fi chipset manufacturers. To overcome these challenges, many techniques have been proposed, such as collaborative positioning techniques, data fusion of radio-based positioning and mobile-based positioning that uses sensors to sense the physical movement activity of users. A few researchers have proposed combining radio-based positioning with vision-based positioning while utilizing image sensors. This work proposed integrated layers of positioning techniques, which is based on enhanced deterministic method; Bayesian estimation and Kalman filter utilising dynamic localisation region. Here, accumulated accuracy is proposed with distribution of error location by estimation at each test point on path movement. The error distribution and accumulated accuracy have been presented in graphs and tables for each result. The proposed algorithm has been enhanced by location based calibration with additional QR calibration. It allows not only correction of the actual position but the control of the errors from being accumulated by utilizing the Bayesian technique and dynamic localisation region. The position of calibration point is determined by analysing the error distribution region. In the last part, modification on Kalman filter step for calibration algorithm did further improve the location error compared to other deterministic algorithms with calibration point. The CDF plots have shown all developed techniques that provide accuracy improvement for indoor positioning based on Wi-Fi fingerprinting and QR calibration

カメラ画像と汎用センサの統合による自動車位置推定の研究

東京海洋大学博士学位論文 平成29年度(2017) 応用環境システム学 課程博士 甲第479号指導教員名: 久保信明全文公表年月日: 2018-06-20東京海洋大学201