Fast fiber coupled clearance profile scanner using real time 3D data processing with automatic rail detection

Blug, A.; Baulig, Claudia; Wölfelschneider, H.; Höfler, H.

doi:10.1109/ivs.2004.1336462

Cited by 10 publications

(10 citation statements)

References 2 publications

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“…Thus, it should be as small as possible. To ensure a reliable detection of objects, they should be hit by at least three beams [16]. In a height of H = 3.5 m with Δj = 0.5° and with negligible spot size, the minimal object size at a width on ground of W = 6 m is 23 cm.…”

Section: Requirements On Lidar Sensors and Market Reviewmentioning

confidence: 99%

“…For example, objects smaller than 2.75 m may be missed at a rate of 30 scans per second, driving with a velocity of 100 km/h, and considering that an object can only be recognized if it was hit by three scans, again. Furthermore, with a higher measurement rate it is easier to drop erroneous measurements [16].…”

Section: Requirements On Lidar Sensors and Market Reviewmentioning

confidence: 99%

“…Motivated by Rahmig et al [7] and Blug et al [16], a template-matching approach detects grooved rails in the normalized distance data. Therefore, the shape of the grooved rail is of interest.…”

Section: Rail Detectionmentioning

confidence: 99%

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Rail detection using lidar sensors

Stein¹,

Spindler²,

Kuper³

et al. 2016

Int. J. SDP

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This article investigates in which way a lidar sensor can be used in a train-borne localization system. The idea is to sense infrastructure elements like rails and turnouts with the lidar sensor and to recognize those objects with a template-matching approach. A requirement analysis for the lidar sensor is presented and a market review based on these requirements is performed. Furthermore, an approach for template matching on lidar scans to recognize infrastructure objects is introduced and its empirical performance is demonstrated based on measurements taken in a light rail environment. The overall goal of the integration of lidar sensors is to fill the sensory gap of existing train localization approaches, which are able to determine the exact, track-selective train position only if highly accurate position measurements from satellite navigation systems are available, which is often not the case. By integrating a lidar sensor, the localization system becomes more diverse, more robust, and can tolerate missing or faulty measurements from the satellite navigation system.

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Section: Requirements On Lidar Sensors and Market Reviewmentioning

confidence: 99%

Section: Requirements On Lidar Sensors and Market Reviewmentioning

confidence: 99%

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Rail detection using lidar sensors

Stein¹,

Spindler²,

Kuper³

et al. 2016

Int. J. SDP

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“…For this purpose it should be as small as possible. To ensure a reliable detection of objects we claim that objects should be hit by at least three beams [14], especially as the train is moving. The smallest detectable object is determined by the sum of the spot size of the beam and the distance between those three beams, which should be as small as possible.…”

Section: Review Of Lidar Sensors For Detecting Railway Infrastructurementioning

confidence: 99%

“…Figure 4(a) shows a series of about 250 scans while passing a turnout trailing. Motivated by [7,14] we used a template matching approach that detected grooved rails in the lidar sensor measurements. Therefore, we required that each groove is hit by at least three beams in the lateral as well as in longitudinal direction.…”

Section: Case Study Of a Lidar Sensor In A Tramway Scenariomentioning

confidence: 99%

An analysis of different sensors for turnout detection for train-borne localization systems

Stein¹,

Lauer²,

Spindler³

2014

WIT Transactions on the Built Environment

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Safe railway operation requires a reliable localization of trains in the railway network. Hence, this paper aims to improve the accuracy and reliability of train-borne localization systems proposed recently. Most of these approaches are based on a global navigation satellite system (GNSS) and odometers. However, these systems turned out to have severe shortcomings concerning accuracy and availability. We believe that the ability to detect turnouts and the branching direction thereon is the most valuable clue for improvement. Knowing the branching direction provides topological information about the train position. Thus, it complements the geographical information of GNSS and the longitudinal position information of odometers in an ideal way. With such a sensor setup a track-selective localization would be possible even if GNSS is unavailable or disturbed. Therefore, this paper compares the individual benefits of different sensor principles for turnout detection such as inertial measurement units (IMUs), cameras, and lidar (light detection and ranging) sensors. As a consequence, we focus on lidar sensors. For those we define requirements, review the market, and report the results of a case study in a tramway scenario. We proved that it is possible to detect rails, turnouts, and platforms. Finally we discuss our findings intensively and give an outlook on our further research.

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Track detection in 3D laser scanning data of railway infrastructure

Häckel

Stein

Maindorfer

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Novel safety systems are needed to meet the growing demand of railway operation. In this paper we introduce general techniques for the detection of tracks and their components in 3D laser scanning data. These techniques make use of feature based methods, such as support vector machines, as well as model based methods, such as template matching. The focus of this work are robust and precise detectors for infrastructure elements, such as rails, tracks, closure rails, and frogs. These parts can be used for both, track maintenance and train-borne localization. The approach is evaluated experimentally on 3D laser scanning data and compared with a reference system. Furthermore, the approach is generic such that it can be used for data of any suitable laser scanning system

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Fast fiber coupled clearance profile scanner using real time 3D data processing with automatic rail detection

Cited by 10 publications

References 2 publications

Rail detection using lidar sensors

Rail detection using lidar sensors

An analysis of different sensors for turnout detection for train-borne localization systems

Track detection in 3D laser scanning data of railway infrastructure

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