8 research outputs found
Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems
Abstract— Nowadays careful measurement applications are
handed over to Wired and Wireless Sensor Network. Taking
the scenario of train location as an example, this would lead to
an increase in uncertainty about position related to sensors
with long acquisition times like Balises, RFID and
Transponders along the track. We take into account the data
without any synchronization protocols, for increase the
accuracy and reduce the uncertainty after the data fusion
algorithms. The case studies, we have analysed, derived from
the needs of the project partners: train localization, head of an
auger in the drilling sector localization and the location of
containers of radioactive material waste in a reprocessing
nuclear plant. They have the necessity to plan the maintenance
operations of their infrastructure basing through architecture
that taking input from the sensors, which are localization and
diagnosis, maps and cost, to optimize the cost effectiveness and
reduce the time of operation
Precise vehicle location as a fundamental parameter for intelligent selfaware rail-track maintenance systems
The rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance.
The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely.
Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either.
Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed
Generating Compact Geometric Track-Maps for Train Positioning Applications
In this paper, we present a method to generate compact geometric track-maps
for train-borne localization applications. Therefore, we first give a brief
overview on the purpose of track maps in train-positioning applications. It
becomes apparent that there are hardly any adequate methods to generate
suitable geometric track-maps. This is why we present a novel map generation
procedure. It uses an optimization formulation to find the continuous sequence
of track geometries that fits the available measurement data best. The
optimization is initialized with the results from a localization filter
developed in our previous work. The localization filter also provides the
required information for shape identification and measurement association. The
presented approach will be evaluated on simulated data as well as on real
measurements
Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints
Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a track-selective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter
Four years of multi-modal odometry and mapping on the rail vehicles
Precise, seamless, and efficient train localization as well as long-term
railway environment monitoring is the essential property towards reliability,
availability, maintainability, and safety (RAMS) engineering for railroad
systems. Simultaneous localization and mapping (SLAM) is right at the core of
solving the two problems concurrently. In this end, we propose a
high-performance and versatile multi-modal framework in this paper, targeted
for the odometry and mapping task for various rail vehicles. Our system is
built atop an inertial-centric state estimator that tightly couples light
detection and ranging (LiDAR), visual, optionally satellite navigation and
map-based localization information with the convenience and extendibility of
loosely coupled methods. The inertial sensors IMU and wheel encoder are treated
as the primary sensor, which achieves the observations from subsystems to
constrain the accelerometer and gyroscope biases. Compared to point-only
LiDAR-inertial methods, our approach leverages more geometry information by
introducing both track plane and electric power pillars into state estimation.
The Visual-inertial subsystem also utilizes the environmental structure
information by employing both lines and points. Besides, the method is capable
of handling sensor failures by automatic reconfiguration bypassing failure
modules. Our proposed method has been extensively tested in the long-during
railway environments over four years, including general-speed, high-speed and
metro, both passenger and freight traffic are investigated. Further, we aim to
share, in an open way, the experience, problems, and successes of our group
with the robotics community so that those that work in such environments can
avoid these errors. In this view, we open source some of the datasets to
benefit the research community
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