4,633 research outputs found
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
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
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
Advanced Train Positioning/Communication System
In the past, in order to ensure train positioning as well as ground-to-train information exchange, railways have adopted various technologies. Over time, each new generation of equipment enriched the global information exchange but, as a consequence, necessitated higher data rate transfers. For the positioning functionality, the existing localisation systems are still limited, since most of them require an infrastructure installation with constraints such as laying equipment between the rails or having high database maintenance requirements and computational costs. Moreover, some of them accumulate errors (odometers and inertial sensors) or offer limited coverage in shadowed areas (GNSS, etc.). Currently, in railway applications, a widely used localization system is based on proprioceptive sensors embarked in the train. This on-board system is coupled to the use of balises located at ground between the rails. These balises are kilometre markers. They are used to compensate for the drift of the localization information computed using the proprioceptive sensors alone, when the train moves. The balises provide absolute localization information whenever the train passes over them. They can also provide spot communication during the short period of time when trains are passing over them. In the first part of this chapter, techniques for achieving train positioning and data exchanges between trains and infrastructure are introduced. In the second part, a new balise is proposed. Particular attention is paid to the contribution of this new solution in terms of localization error and communication performances
Passive radar based on WiFi transmissions: signal processing schemes and experimental results
Aim of this work is to study innovative techniques and processing strategies for a new passive sensor for short range surveillance. The principle of work of the sensor will be based on the passive radar principle, and WiFi transmissions - which usually provide Internet access within local areas - will be exploited by the passive
sensor to detect, localize and classify targets
Passive radar based on WiFi transmissions: signal processing schemes and experimental results
Aim of this work is to study innovative techniques and processing strategies for a new passive sensor for short range surveillance. The principle of work of the sensor will be based on the passive radar principle, and WiFi transmissions - which usually provide Internet access within local areas - will be exploited by the passive
sensor to detect, localize and classify targets
Real-time Monocular Object SLAM
We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques
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