Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map

Train localization is safety-critical and therefore the approach requires a continuous availability and a track-selective accuracy. A probabilistic approach is followed up in order to cope with multiple sensors, measurement errors, imprecise information, and hidden variables as the topological position within the track network. The nonlinear estimation of the train localization posterior is addressed with a novel Rao-Blackwellized particle filter (RBPF) approach. There, embedded Kalman filters estimate certain linear state variables while the particle distribution can cope with the nonlinear cases of parallel tracks and switch scenarios. The train localization algorithmis further based on a trackmap andmeasurements froma GlobalNavigation Satellite System(GNSS) receiver and an inertial measurement unit (IMU). The GNSS integration is loosely coupled and the IMU integration is achieved without the common strapdown approach and suitable for low-cost IMUs.The implementation is evaluated with realmeasurements from a regional train at regular passenger service over 230 km of tracks with 107 split switches and parallel track scenarios of 58.5 km.The approach is analyzed with labeled data by means of ground truth of the traveled switch way. Track selectivity results reach 99.3% over parallel track scenarios and 97.2% of correctly resolved switch ways

A review on technologies for localisation and navigation in autonomous railway maintenance systems

Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle’s position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.European Union’s Horizon 2020 research and innovation programme. Shift2Rail Joint Undertaking (JU): 88157

Mobile laser scanning based determination of railway network topology and branching direction on turnouts

GNSS is often inaccurate and satellite signals are not always available, which results in ambiguous situations. In order to reduce their negative effects on train-borne localization, this work proposes an approach for the detection of tracks, turnouts, and branching directions solely from 2d lidar sensor measurements. The experimental evaluation shows highly correct and complete results. In summary, these detections are sufficient to reduce ambiguity problems in train-borne localization

Autonomous navigation and multi-sensorial real-time mocalization for a mobile robot

Doutoramento em Engenharia MecânicaO principio por detrás da proposta desta tese é a navegação de ambientes utilizando uma sequência de instruções condicionadas nas observações feitas pelo robô. Esta sequência é denominada como uma 'missão de navegação'. A interacção com um robô através de missões permitirá uma interface mais eficaz com humanos e a navegação de ambientes de maior escala e duma forma mais simplificada. No entanto, esta abordagem abre problemas novos no que diz respeito à forma como os dados sensoriais devem ser representados e utilizados. Neste trabalho representações binárias foram introduzidas para facilitar a integração dos dados multi-sensoriais, a dimensionalidade da qual foi reduzida através da utilização de Misturas de Distribuições de tipo Bernoulli. Foi também aplicada a técnica de cadeias de Markov ocultas (Hidden Markov Models), que contou com o desenvolvimento e a utilização dum modelo de cadeia de Markov original, esta que consegue explorar a informação contextual da sequência da missão. Uma aplicação que surgiu da aplicação do método de localização foi a criação de representações topologicas do ambiente sem ter que previamente recorrer à criação de mapas geométricos. Outras contribuições incluem a aplicação de métodos para a extracção de propriedades locais em imagens e o desenvolvimento de propriedades extraídas a partir de varrimentos dum medidor de distancia laser.This thesis evaluates the requisites for the specification of mobile robot 'Missions' for navigation within environments that are typically used by human beings. The principal idea behind the proposal of this thesis was to allow localization and navigation by providing a sequence of instructions, the execution of each instruction being conditional on the expected sensor data. This approach to navigation is expected to lead to new applications which will include the autonomous navigation of environments of very large scale. It is also expected to lead to a more intuitive interaction between mobile robots and humans. However, the concept of the navigation Mission opens up new problems namely in the way in which the sequence of instructions and the expected observations are to be represented. To solve this problem, binary features were used to integrate observations from multiple sensors, the dimensionality of which was reduced by modelling the binary data as a Finite Mixture Model comprised of Bernoulli distributions. Another original contribution was the modification of the Markov Chains used in Hidden Markov Models to enable the use of the sequential context in which the expected observations are specified in the navigation Mission. The localization method that was developed enabled the direct creation of a topological representation of an environment without recourse to an intermediate geometric map. Other contributions include developments that were made in the characterisation of images through the application of local features and of laser range scans through the creation of original features based on the scan contour and free-area properties

Wirbelstromsensorbasierte Lokalisierung von Schienenfahrzeugen in topologischen Karten

Diese Arbeit beschreibt ein Verfahren für die präzise Lokalisierung von Schienenfahrzeugen unter alleiniger Nutzung eines Wirbelstromsensorsystems. Die Geschwindigkeit wird mit Laufzeitkorrelationsverfahren und einem Kalman-Filter gewonnen. Die Erkennung der Weichen wird durch den Einsatz verdeckter Markowmodelle erreicht. Die Fusion in einer topologischen Karte, basierend auf sequentiellen Monte Carlo Verfahren, liefert schließlich die gewünschte Positionsangabe