This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feedin...

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Perspectives on railway track geometry condition monitoring from in-service railway vehicles

Railways have already introduced satellite-based localization systems for non-safety related applications. Driven by economic reasons, the use of these systems for new services and, in particular, their introduction in signaling system is seriously investigated today and tested all around the world. Because of the weight of their history, their strong normative context, and the high requested level of safety, the introduction is relatively slow. The aim of this paper is to provide a survey of past and current programs dealing with global navigation satellite systems as a basis to introduce main issues relative to context, standards, performance requirements, and safety proofs. Links with aeronautical concepts are also presented, illustrating the transposable principles and the limits due to the land transport environment.

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A Survey of GNSS-Based Research and Developments for the European Railway Signaling

We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map.

Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments

Positioning accurately and safely a train is nowadays a great challenge. That includes currently available railway sensors and new candidate sensors for data fusion. Global Navigation Satellite System and Inertial Measurement Unit sensors arise as prominent technologies to incorporate in railways. Although satellite-based train localization tests can be found in the scientific literature, there are no common criteria to evaluate the performance of the positioning achieved. In this paper, a series of criteria is defined and justified in order to be able to evaluate the most recent and relevant works related to train positioning. The results of this comparative analysis are gathered in tables, where the criteria defined are applied to the works compiled. According to the results obtained, a research gap in safety related applications is found. It is concluded that the economic viability of given solutions should be explored, so as to design an on-board train-integrated positioning system.

A Survey of Train Positioning Solutions

Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of the Wi-Fi wireless network. This article presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m with a step counter-based PDR.

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Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

This paper presents measurements of train motion with a low-cost inertial measurement unit (IMU) based on micro electro mechanical systems (MEMS). The measurements were recorded on-board a train during normal passenger transport service on a network with dense urban railway environment as well as a rural, regional network environment. Sensor measurements from several train runs were therefore analyzed and the data is presented with a discussion on typical characteristics, noise and dynamics. As the train motion is dependent on the track, local track characteristics are inferred from the train motion measurements. Finally, the inertial measurements are analyzed toward track feature detection for feature based localization purposes.

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Measurement and analysis of train motion and railway track characteristics with inertial sensors

The avoidance of train collisions is vital for human safety in railway transportation. Technical approaches are general train control or collision avoidance systems as well as semi-automated or fully autonomous trains. These systems rely on robust and exact train localization as well as an accurate map of the track network.We present Simultaneous Localization and Mapping relying exclusively on train-side sensors. RailSLAM, implemented as a probabilistic filter, uses measurements from multiple sensors and computes a track map. We rely heavily on sensors that are not affected by the harsh environmental conditions often experienced in this application, in particular a low-cost MEMS Inertial Measurement Unit (IMU). Rail vehicle localization methods based on these sensors require a dedicated map with detailed geometric track features in combination with the topological track connections. If this feature map does not exist apriori, it needs to be created. If it does, it may suffer from incompleteness, insufficient accuracy or outdated information. RailSLAM addresses the creation and maintenance of this special track map by a simultaneous estimation of the probabilistic geometric-topological feature-rich track map and the train state. A first proof of concept implementation of mapping is given based on the use of an Extended Kalman Filter with measurements from Global Navigation Satellite System (GNSS) and an IMU.

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RailSLAM - Localization of rail vehicles and mapping of geometric railway tracks

The use of foot-mounted inertial measurement units (IMUs) has shown promising results in providing accurate human odometry as a component of accurate indoor pedestrian navigation. The specifications of these sensors, such as the sampling frequency have to meet requirements related to human motion.

Optimal sampling frequency and bias error modeling for foot-mounted IMUs

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 algorithm is further based on a track map and measurements from a Global Navigation 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 real measurements 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.

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

The localization of trains in a railway network is necessary for train control or applications like autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, accurate and track selective localization. The proposed approach focuses on an onboard localization system using appropriate onboard sensors without any additional railway infrastructure. Global navigation satellite systems (GNSS) are very beneficial for this task, but the accuracy, measurement errors and the lack of availability in parts of the railway environment do not fulfill the requirements for a safety critical railway system. An inertial measurement unit (IMU) is used to increase robustness and accuracy, by sensing the position related effects of 3D track geometry on the train. In order to cope with multiple sensors, position related uncertainties and sensor errors, we propose a probabilistic approach with a Bayesian filter. In this paper we present a train localization approach using a particle filter in combination with multiple onboard train sensor measurements and a known track map. The particle filter estimates a topological position directly in the track map. First simulations show encouraging results in robustness and accuracy in critical railway scenarios.

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Oliver Heirich

Papers

Measurement and analysis of train motion and railway track characteristics with inertial sensors

RailSLAM - Localization of rail vehicles and mapping of geometric railway tracks

Optimal sampling frequency and bias error modeling for foot-mounted IMUs

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

Bayesian train localization method extended by 3D geometric railway track observations from inertial sensors