System and method for mapping a railway track

A method and a system (30) for inspecting and/or mapping a railway track (18). The method comprises: acquiring geo-referenced rail geometry data associated with geometries of two rails (20) of the track along the section; acquiring geo-referenced 3D point cloud data, which includes point data corresponding to the two rails and surroundings of the track along the section; deriving track profiles of the track from the geo-referenced 3D point cloud data and the geo-referenced rail geometry data; and comparing the track profiles and generating enhanced geo-referenced rail geometry data and/or enhanced geo-referenced 3D point cloud data based on the comparison.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national stage application of International Application No. PCT/NL2018/050304, which was filed on May 9, 2018, which claims priority to Netherlands Application Number 2018911 filed on May 12, 2017, both of which are incorporated by reference in their entireties.

TECHNICAL FIELD

The invention relates to a system and a method for mapping a railway track. Furthermore, the invention relates to a computer program product arranged to perform the proposed method, and a computer readable medium comprising such a computer program.

BACKGROUND ART

Railway tracks require regular inspection to allow timely detection of problems relating to impending track failure. Failure or misalignment of the track may be caused by wear of the rails, deterioration of the sleepers, damaged or disconnected rail fasteners, or by displacement (e.g. subsidence) of the track bed or underlying soil and support structures.

Systems and methods for automated inspection of railway tracks and analyzing inspection data are known. One goal of such automated systems is non-destructive and high-speed assessment of railway tracks. Inspection systems typically use sources of coherent light to illuminate regions of the railway track during inspection runs.

Patent document WO2009/064177A1 describes an appliance for measuring rail geometry, which can be quickly attached to an automatic coupling of a standard train wagon in such a way that it is completely carried by the automatic coupling. The known appliance comprises a laser measuring system for measuring a location of the rail relative to the appliance, and an inertial measuring system for determining the geographic location of the appliance. Combination of the geographic location of the appliance and position of the rail relative to the appliance allows determination of the geographic position of the rail. WO2009/064177A1 provides little information in relation to the imaging and mapping of the railway track.

It would be desirable to provide a system and a method that allow mapping of a railway track with high accuracy.

SUMMARY OF INVENTION

The invention provides a system and a method for mapping the geometry of a railway track using railway vehicle mounted equipment. The system and method allow accurate mapping of railway track geometry, and detection of various rail displacements and irregularities.

According to a first aspect, there is provided a method for mapping a section of a railway track. The method comprises: —acquiring geo-referenced rail geometry data associated with geometries of two rails of the track along the section; —acquiring geo-referenced three-dimensional (3D) point cloud data, which includes point data corresponding to the two rails and surroundings of the track along the section; —deriving track profiles of the track from the geo-referenced 3D point cloud data and the geo-referenced rail geometry data, and —comparing the track profiles and generating enhanced geo-referenced rail geometry data and/or enhanced geo-referenced 3D point cloud data based on the comparison.

According to an embodiment, the geo-referenced 3D point cloud data includes point data corresponding to two further rails along a co-extending section of an adjacent railway track. The method may comprise: —acquiring further geo-referenced rail geometry data associated with geometries of the two further rails of the adjacent railway track; —deriving further track profiles of the adjacent track from the geo-referenced 3D point cloud data and the further geo-referenced rail geometry data. The comparing may then include determining distance profiles associated with transverse distances and/or elevation differences between the track profiles and the further track profiles. The generating may then be based on the distance profiles.

According to a further embodiment, the track profiles comprise a first centerline profile of the track in the geo-referenced rail geometry data, and a second centerline profile of the track in the geo-referenced 3D point cloud data. The further track profiles comprise a further centerline profile of the adjacent track in the further geo-referenced rail geometry data, and an adjacent centerline profile of the adjacent track in the geo-referenced 3D point cloud data. The distance profiles may then comprise a first distance profile defined between the first centerline profile and the further centerline profile, and a second distance profile defined between the second centerline profile and the adjacent centerline profile.

According to further embodiments, generating the enhanced geo-referenced rail geometry data includes adjusting geo-reference correspondences for the geo-referenced rail geometry data and the further geo-referenced rail geometry data, to let the first distance profile converge towards the second distance profile.

According to yet a further embodiment, the geo-reference correspondences are adjusted based on weighted contributions, including a first weight associated with the track profile of the track in the geo-referenced rail geometry data, and a second weight associated with the further track profile of the adjacent track in the further geo-referenced rail geometry data.

According to embodiments, the geo-referenced rail geometry data comprises a plurality of overlapping data sets associated with the section of the track, and the track profile is an average of track profiles for the overlapping data sets. In addition, the further geo-referenced rail geometry data may comprise a plurality of further overlapping data sets associated with the co-extending section of the adjacent track, and the further track profile is an average of further track profiles for the further overlapping data sets.

According to a further embodiment, the first weight is a quantity of dispersion for the overlapping data sets with respect to the average of the track profiles. The second weight is a quantity of dispersion for the further overlapping data sets with respect to the average of the further track profiles.

According to further embodiments, generating the enhanced geo-referenced 3D point cloud data includes adjusting geo-reference correspondences for the geo-referenced 3D point cloud data, to let the second centerline profile and adjacent centerline profile converge towards the first centerline profile and the further centerline profile, respectively.

According to embodiments, the method comprises generating composite track data by merging the enhanced geo-referenced rail geometry data and the enhanced geo-referenced 3D point cloud data into a single dataset.

According to embodiments, acquiring geo-referenced rail geometry data comprises: —acquiring orientation data and position data at a plurality of locations along the section of the track; —acquiring two-dimensional (2D) images including outlines of both rails at or near the plurality of locations along the track, and —generating the geo-referenced rail geometry data, by combining the set of 2D images with the orientation and position data.

In a further embodiment, acquiring 2D images comprises: —projecting at least one collimated light beam towards each or both of the two rails of the track, and —receiving reflected beam portions from the respective rails, to acquire reflection image data at or near the plurality of locations along the section of the track;

In an alternative further embodiment, acquiring 2D images comprises: —scanning at least one laser beam transversely across each or both of the two rails, and —receiving reflected beam portions from the respective rails, to acquire ranging data at or near the plurality of locations along the section of track.

According to embodiments, acquiring geo-referenced 3D point cloud data comprises: —acquiring orientation data and position data at a plurality of locations along the section of the track; —scanning a laser beam across the two rails and a portion of the surroundings of the track; —detecting reflections of the laser beam from the two rails and the surroundings, to acquire ranging data that includes point data corresponding to the two rails and surroundings along the section of the track, and —generating the geo-referenced 3D point cloud data by combining the ranging data with the orientation and position data.

According to a further embodiment, acquiring georeferenced 3D point cloud data further comprises: —scanning the laser beam across two further rails of a co-extending section of an adjacent railway track, and; —detecting reflections of the laser beam from the two further rails, so that the acquired ranging data also includes point data corresponding to the two further rails along the co-extending section of the adjacent track.

According to a second aspect, there is provided a system for mapping a section of a railway track, which is configured to execute the method according to the first aspect.

The system may comprise: —a positioning device configured to acquire orientation data and position data at a plurality of locations along the section of the track while the system is moved along the track; —a first acquisition device configured to acquire 2D images including outlines of two rails at or near the plurality of locations; —a second acquisition device configured to acquire 3D laser ranging data including point data corresponding to the two rails and surroundings along the section of the track, and —a processing device. This processing device is configured to: —generate geo-referenced rail geometry data associated with geometries of two rails of the track along the section, by combining the set of 2D images with the orientation and position data; —generate geo-referenced 3D point cloud data, which includes point data corresponding to the two rails and surroundings of the track along the section; —derive track profiles for the track from the geo-referenced 3D point cloud data and the geo-referenced rail geometry data, and —compare the track profiles and generate enhanced geo-referenced rail geometry data and/or enhanced geo-referenced 3D point cloud data based on the comparison.

According to an embodiment, the second acquisition device is a laser scanner, which is configured to scan a laser beam across the two rails and a portion of the surroundings of the track, and across two further rails of a co-extending section of an adjacent railway track, and configured to detect laser beam reflections and acquire ranging data that includes point data corresponding to the two rails and the surroundings along the section of the track, as well as the two further rails along the co-extending section of the adjacent track.

According to embodiments, the system comprises a frame with a mounting mechanism for attaching the system to a railway vehicle, wherein the positioning device, the first acquisition device, and the second acquisition device are fixed to the frame at predetermined positions.

According to a third aspect, there is provided a railway vehicle including: —a vehicle coupling mechanism at a front side or rear side of the railway vehicle, and —a system for mapping a railway track according to the second aspect, and attached to the railway vehicle via the coupling mechanism.

According to a fourth aspect, there is provided a computer program product configured to provide instructions to carry out a method according to the first aspect, when loaded on a computer arrangement.

According to a fifth aspect, there is provided a computer readable medium, comprising a computer program product according to the fourth aspect.

The figures are meant for illustrative purposes only, and do not serve as restriction of the scope or the protection as laid down by the claims.

DESCRIPTION OF EMBODIMENTS

The following is a description of certain embodiments of the invention, given by way of example only and with reference to the figures. It may be helpful to an understanding of the invention to set forth definitions of certain terms to be used herein.

The terms “track”, “railway track”, and “railroad track” are used herein interchangeably, to refer to a railway portion including two rails, the interconnecting (cross-)ties, the components that fix the rails to the ties, and ballast material.

The term “mapping” (in relation to the track and/or its surroundings), is used in a broad sense to indicate coordinate-referenced imaging of the track and/or its surroundings, and/or coordinate-referenced description of railway track parameters (e.g. gauge, centerline, cant).

The term “(rail) gauge” is used herein to indicate a transversal distance (width) between the inner gauge surfaces of the two rails belonging to the same track. Unless explicitly indicated otherwise, this term refers to a local gauge, which is represented by a parameter value that may vary along the track. Typically, such variations must remain within a predetermined range of acceptable gauge values.

The term “(track) centerline point” is used herein to indicate a nominal point at exactly half the rail gauge away from the inner gauge surface of either rail of the same track. The centerline point is a local spatial characteristic of the track. A collection of local track centerline points belonging to the same track may be combined to form a “(track) centerline”, which defines a three-dimensional trajectory associated with this track.

The term “(track) cant” is used herein to indicate a height difference between the upper surfaces of the two rails belonging to the same track. Unless explicitly indicated otherwise, this term refers to a local cant, which is represented by a parameter that may vary along the track. Typically, such variations must remain within a predetermined range of acceptable cant values, for example within a range of −150 millimeters to +150 millimeters (including end points). In a straight portion of the track, the local cant is preferably close or even equal to 0 millimeters.

The term “surroundings of the track” refers herein to a region that directly surrounds the track within a horizontal distance of at least 10 meters from the track centerline. One or more neighboring tracks may be present within this surrounding region, and the track and its neighboring track(s) may be imaged simultaneously. The achievable coverage of the surrounding region depends on the achievable range and scanning resolution of the image acquisition devices. Preferably, the surrounding region covers an area within a horizontal distance of up to 25 meters from the track centerline, or more.

The term “outline” is used herein to refer to a curve corresponding to the outer boundary surface of a body. The term “surface” is used herein to generally refer to a two-dimensional parametric surface region, which may have an entirely flat (i.e. a plane) or piece-wise flat shape (e.g. a polygonal surface), a curved shape (e.g. cylindrical, spherical, parabolic surface, etc.), a recessed shape (e.g. stepped or undulated surface), or a more complex shape. The term “plane” is used herein to refer to a flat surface that is unambiguously defined by three non-collinear points.

In the next figures, a local system with Cartesian coordinates will be used to describe spatial relations for exemplary embodiments of the inspection system and method. The longitudinal direction X corresponds to the local direction of movement of the railway vehicle or inspection system along the track. Transversal direction Y is perpendicular to the longitudinal direction X, and vertical direction Z is perpendicular to X and Y. The terms “front” and “rear” relate to longitudinal direction X, “left”, “right”, “lateral” relate to transversal direction Y, and to “above” and “below” relate to vertical direction Z. It should be understood that the directional definitions and preferred orientations presented herein merely serve to elucidate geometrical relations for specific embodiments. The concepts of the invention discussed herein are not limited to these directional definitions and preferred orientations. Similarly, directional terms in the specification and claims are used herein solely to indicate relative directions and are not otherwise intended to limit the scope of the invention or claims.

FIG.1schematically shows a perspective view of an embodiment of a system30for mapping a railway track18.FIG.2presents a frontal cross-section of this railway mapping system30. The system30is configured to survey the track18and to acquire data relating to the geometry of the track18and objects in the direct vicinity of the track18, and further data relating to position and/or orientation of the system30relative to the track18. The track18includes a first rail20aand a second rail20b, which are interconnected and held in place by a plurality of crossties28, and which are supported by an underlying track bed26. A second railway track19with two rails21a,21bextends alongside the track18.

The exemplary system30shown inFIG.1comprises two light projector devices40a,40b(e.g. laser fan beam projectors) for generating and projecting collimated light beams42a,42btowards the track18, two image acquisition devices46a,46b(e.g. cameras) for receiving light reflected by the rails20a,20b, two laser scanners50a,50bfor acquiring three-dimensional image data of the surroundings, a positioning device60for acquiring the position/orientation data, a processing device80, and a data storage device82.

The inspection system30comprises a rigid frame32, to which the light projectors40, the cameras46, and the laser scanners50are attached at predetermined positions. The inspection system30also includes a mounting mechanism38for releasably attaching the inspection system30with its frame32to a railway vehicle10, which is adapted for travel over and along the railway track18. In the coupled state, the mounting mechanism38allows the inspection system30to be moved as an integrated unit together with the railway vehicle10, as the vehicle10moves on and along the track18. In this exemplary embodiment, the mounting mechanism38is adapted to be mounted to the automatic coupling of a standard train wagon, at a front or rear side thereof.

The exemplary system30shown inFIGS.1and2is configured to execute various method steps, and to acquire, generate and/or process various data types during method execution. Method steps and data types will be discussed with reference toFIGS.3-5, and indicated with reference numerals preceded by 100 or 200 thereof.

In the exemplary system30, the processing device80is installed in an enclosed center region of the frame32. In alternative system embodiments, the processing device may be an integral part of the camera system46or the laser scanner system50. In yet alternative system embodiments, the processing device may be part of a computer device that is not mechanically coupled to the frame32, but which forms a spatially separate unit. Such a computer device is provided with a data communication interface, which allows data acquired and generated by the system30to be retrieved and processed remotely, either via real-time processing or via offline (post-)processing.

Similarly, the data storage device82forms a distinct storage unit that is installed in the enclosed center region of the frame32, and which allows the data acquired and generated by the system30to be stored for further processing purposes. In alternative systems that are configured for real-time processing by a remote computer device, the storage device may function as a temporary data buffer, while acquired and/or generated data is scheduled for transmission to the remote computer device. The acquired and/or generated data may then be transmitted (e.g. via a 3G, 4G, or WiFi-based communication device) to the remote computer, while the inspection run is still in progress. In yet alternative system embodiments that are configured for offline processing by a remote computer device, the storage device may have a considerable data storage capacity, so that all the data that is acquired and/or generated during inspection runs can be stored and transferred to the remote computer device after the inspection runs have been completed.

During an inspection run, the train10and the system30are moved along the track18(or the second track19). The positioning device60includes an inertial measurement unit (IMU)62and a Global Navigation Satellite System (GNSS) receiver64. In the exemplary system30shown inFIGS.1-2, the IMU62is installed in an enclosed center region of the frame32, at a predetermined fixed position relative to the frame32. The IMU62is configured to dynamically gather data106of the relative orientation of the IMU62and the frame32as a function of time. The IMU62may comprise gyroscopes, to measure pitch, roll, and yaw angles of the frame32relative to a preset reference orientation. Alternatively or in addition, the IMU62may comprise accelerometers for recording and integrating accelerations of the frame32, to calculate displacements of the frame32relative to a preset reference.

In this example, the GNSS receiver64is also installed in the enclosed center region of the frame32, at a predetermined fixed position relative to the frame32. The GNSS receiver64is coupled to and in signal communication with a GNSS antenna66, which is fixed to an upper side34of the frame32via a pole67. The GNSS receiver64and antenna66are jointly configured to receive GNSS signals69from a plurality of GNSS satellites68, and to use these GNSS signals69to dynamically determine geospatial positions of the GNSS receiver64and the fame32, while the mapping system30is moved along the track20. The associated system position data108is continuously collected and stored in the storage device82. The GNSS antenna66is located at a non-zero distance above the upper frame side34, to reduce interference effects for the received GNSS signal69caused by the train10(e.g. by partial EM shielding and/or multipath effects). In this example, the vertical distance is about 110 centimeters. In other embodiments, the GNSS antenna may be positioned at different vertical distances, depending on train type, regulations, or other conditions.

The processing device80is in signal communication with the IMU62and the GNSS receiver64, to receive the orientation data106and the position data108. The orientation data106and position data108are combined by the processing device80, in order to accurately determine relative positions and orientations of the system30as a function of time, while the system30travels with the train10along the track18.

In this example, the two light projector devices40are laser projectors40, which are configured to generate respective fan beams42a,42b. The term “fan beam” refers herein to a beam of light that propagates along a central axis A, and which has asymmetric cross-sectional intensity profiles in planes perpendicular to this axis A. These cross-sectional intensity profiles are elongated, with a first characteristic dimension in a perpendicular direction that is at least an order of magnitude larger than a second characteristic dimension in the other perpendicular direction. The cross-sectional intensity profiles may for example have a rectangular, elliptical, or stadium shape. The cross-sectional intensity profile of the fan beam may widen as it propagates along the central axis A. An angular spread Δψ may be used to (approximately) describe the divergence of the first characteristic dimension along the axis A.

The lasers projectors40may comprise laser sources with a peak optical output power of 1 Watt. The processing device80may be in signal communication with the laser projectors40, and configured to control light emission characteristics, such as light intensity parameters and/or directionality and width of the generated fan beams42. Each laser projector40is positioned at a lower side33of the frame32, and is configured to project its fan beam42with a downwards component (in the negative vertical direction −Z) towards a portion of the track18including at least one of the rails20a,20b. In this example, the fan beams42are aimed to cover at least an upper edge portion22and an inner lateral edge portion24of the associated rail20. Preferably, each light projector40projects its fan beam42in a slant direction along a substantially vertical imaging plane along the transversal and vertical directions Y and Z. Each fan beam42extends along its axis A, which is tilted at an angle ψ of about 30° with respect to the vertical direction Z, and has an angular spread ψ of about 75° in the image plane around the axis A. The fan beam42intersects the corresponding rail20in such a way that the larger characteristic dimension of the fan beam42extends essentially perpendicular to the longitudinal direction X of the rail20.

A portion of the field of each fan beam42will be reflected off the corresponding rail20and the track bed26. This creates light reflection curves44a,44bon the rails20and the track bed26, which follow the local surface contours of the rails20and the track bed26.

As shown inFIG.2, the two cameras46a,46bare also fixed to the lower side33of the frame32. Each camera46is positioned diagonally above a respective rail20of the track18. Two distinct camera positions are used to capture reflection image data116for each of the two rails20, at or near various positions Xi along the track18(i being a discrete index). The first camera46ais directed with its optical axis towards the expected mean position of the first rail20a, and the second camera46bis directed with its optical axis towards the expected mean position of the second rail20b. The cameras46are configured to capture reflection image data116of the associated light reflection curves44on the rails20and the track bed26.

The cameras46are optically sensitive in a wavelength range that overlaps with the wavelength distribution in the fan beams42. For example, the cameras46may sense visible light emitted by optical light projectors40. Alternatively or in addition, infrared lasers and infrared cameras may be used. The cameras46may include band-pass filters that allow only the electromagnetic wavelengths of the light projectors40to pass, while rejecting other wavelengths, in order to reduce image noise from ambient light conditions. The cameras46are each configured to sample reflection image data116at a high resolution (e.g. >1 Megapixel) and at a significant frame rate (e.g. a rate of about 500 frames per second). In this exemplary system, the inter-image spatial resolution along the track18can be characterized by distance intervals Δx between two adjacent reflection curve images in the image data set116, which are acquired at or near the positions Xi along the track18. For a train10that travels in the longitudinal direction X along the track18at a speed v in a range between 50 to 150 kilometers per hour, the inter-image spatial resolution is expected to be in a range of about 0.03 meters to 0.08 meters.

The cameras46are in signal communication with the data storage device82, to transmit the acquired rail reflection image data116and allow the storage device82to store such rail reflection image data116for further processing purposes. In turn, the data storage device82is in signal communication with the processing device80, to provide stored rail reflection image data116to the processing device80on request.

The processing device80is configured to analyze the rail reflection image data116from each of the cameras46, to determine rail alignment metrics. The processing device80may be configured to detect edge contours and/or points for the rails20in the reflection curve images116, and to establish correspondences between such edge contours/points and expected contours22,24of the rails20. Specific detection points in the rail reflection image data116may for example be matched (e.g. via known image registration techniques) to the upper edge portions22and the lateral inner edge portions24of the rails20. The processing device80may associate particular images from the reflection image data116with particular orientation and position data entries106,108corresponding to particular locations along the track18. This allows the reflection image data116to be correlated with system kinematics, to generate rail geo-referenced rail geometry data122. The processing device80may further be configured to determine spatial dimensions between detected rail contours and/or points from the rail geometry data122, to derive various rail geometry profiles128that describe particular geometry characteristics and their evolution as function of position along the track18. The processing device80may further be configured to compare such rail geometry profiles128with predetermined profiles and dimensional ranges, in order to assess whether the measured rail dimensions are within acceptable ranges.

The two laser scanners50are provided at a front side35of the frame32. Each of the laser scanners50is adapted to dynamically acquire laser reflection/ranging data146from objects in the surroundings of the track18. The first laser scanner50ais located near a first lateral side of the frame32, and is configured to acquire laser reflection/ranging data146aof a first portion of the surroundings that includes the rails20. Similarly, the second laser scanner50bis located near a second lateral side of the frame32, which is laterally opposite to the first lateral side. The second laser scanner50bis configured to acquire laser reflection/ranging data146bof a second portion of the surroundings that also includes the rails20. This arrangement of the laser scanners50allows better spatial coverage and acquisition of more laser reflection data points.

Each of the laser scanners50includes a laser source51and a laser detector52. Each laser source51is adapted to be rotated over 360° about a respective scan rotation axis B, and to emit a laser beam54(not shown) in a direction that is essentially perpendicular to and radially away from this scan rotation axis B. The rotatability of the laser source51allows the laser beam54to be swept along an angular direction around the scan rotation axis B. The emitted laser beam54may have a pulsed character or a continuous wave character. During scanning, each laser beam54is rotated to trace out a circular trajectory around the scan rotation axis B. The laser detector52is configured to detect a beam portion that is (specularly) reflected by a small patch of a structure (“point of reflection”) within the track surroundings, back towards the laser scanner50. When the railway vehicle10and system30are moved along the track18during an inspection run, the rotating laser beams54will trace out skewed helical trajectories, if viewed in a track-based coordinate frame.

In the exemplary system30ofFIGS.1-2, the two scan rotation axes Ba, Bb of the respective laser scanners50a,50bboth extend from the front side35of the frame32, with a large component along the positive longitudinal direction +X and with a smaller component along the negative vertical direction −Z. In addition, the scan rotation axis B of each laser scanner50extends with a smaller component outwards in the transverse direction (i.e. the first scan rotation axis Ba towards the negative transverse direction −Y, and the second scan rotation axis Bb towards the positive transverse direction +Y). The resulting inclined outward arrangement of the laser scanners50ensures that a good field of view is obtained, and increases the likelihood of detecting certain objects in the surroundings of the track18(e.g. objects that extend predominantly vertical and are arranged along or perpendicular to the track18). The spatial configuration of the laser scanners50allows light detection and ranging of the track18and its surroundings, while minimizing shadowing effects (i.e. obstruction of the two laser scanners50by each other, by the frame32, or by the railway vehicle10). The resulting spatial configuration thus allows optimal scanning coverage of the surroundings.

During scanning, each laser scanner50rotates at a speed of about 12000 rotations per minute (rpm), while the laser source51emits the laser beam and the laser detector52simultaneously senses laser beam reflections. In this example, a pulsed laser scanner50is used. In this case, a time difference between the emission and subsequent reception of a laser beam pulse is used to compute a distance between the laser source51and the point of reflection. Each laser scanner50is adapted to detect and record 1 million reflection points per second during scanning. In systems with a continuous wave laser source, beam-focusing effects may be measured by the laser detector to determine ranging distances.

Each detected reflection point in the laser ranging data146can be associated with a position in 3D space, by correlating the predetermined position of the respective laser scanner50(relative to the positioning device60) with the orientation and position data106,108from the positioning device60. This allows generation of a geo-referenced three-dimensional point cloud150of the reflection points along the surveyed portion of the surroundings of the track18. This georeferenced 3D point cloud data150can be used to analyze track layout and the positions of structures that surround the track18.

The system30further comprises a panoramic camera70, which is mounted at the front side35near a central upper region of the frame32. The panoramic camera70may be used to augment the 3D point cloud data150with panoramic image data of the area in front of the train10. The panoramic image data may be used for inspection and visualization, and/or for coloring the 3D point cloud data150.

FIG.3shows a flow diagram for an exemplary method100for mapping a section of a railway track18. The exemplary method100includes:

moving102a mapping system30along a plurality of locations Xi within the track section;

acquiring104system orientation data106and system position data108associated with positions and orientations of the mapping system30at or near the plurality of locations Xi;

acquiring110geo-referenced rail geometry data122associated with geometries of the first and second rails20a,20bat or near the plurality of locations Xi along the track18;

acquiring140geo-referenced 3D point cloud data150, which includes point data corresponding to the two rails20and surroundings of the track18along the section;

deriving126,154track profiles128,156for the track18in the geo-referenced rail geometry data122and the geo-referenced 3D point cloud data150, and

In this example, acquisition104of orientation data106and position data108involves combining the orientation data106from the IMU62and the position data108from the GNSS receiver64, to calculate exact orientation coordinates (e.g. Euler angles, or pitch, roll, and yaw) and position coordinates (e.g. Xr, Yr, Zr) for the frame32of the mapping system30, at the plurality of locations Xi within the section of the track18. Accurate determination of positions may rely on additional data that is received from a reference GNSS network, to supplement the GNSS signals69received directly from the plurality of GNSS satellites68. The position and orientation of the system30may thus be expressed as a function of location within the section of the track18, to yield system trajectory data. Such system trajectory data may for example be expressed in a coordinate system that is fixed with respect to the track20. This fixed coordinate system may for example be a global coordinate system, like ERTS89 in Europe or NAD83 in the United States of America. Absolute positions and orientations for the system30may be re-calculated in a post-processing stage, to improve accuracy of the orientation and position data106,108.

In the example shown inFIG.3, the acquisition110of geo-referenced rail geometry data122includes acquisition114of two-dimensional images116with outlines of both rails20at or near the plurality of locations along the track18, and generation120of the geo-referenced rail geometry data122by combining the set of 2D images116with the orientation106and position data108.

In this example, the 2D images116are acquired114by projecting112fan beams of light42onto the rails20at the plurality of locations Xi along the track20(by continuously or intermittently irradiating the rails20with the light beams42from the projectors40), while the system30is moved along the track18. Beam reflections form the respective rails20may then be received, to acquire114the reflection image data116.

The rail reflection image data116acquired in step114may for example include reflections by upper and lateral edge portions22,24of the rails20. Detection118of rail edges in the reflection image data116may involve the use of automated machine vision techniques, e.g. based on edge detection and/or shape recognition algorithms. The processor device80may for example be configured examine the intensity and/or color of each pixel in the reflection images116, to identify regions in the reflection images116that correspond to the reflection curves44generated by the fan beams42. More advanced techniques, like gradient-based image filtering, shape matching, etc. may be used.

The edge detection data is then combined with the orientation and position data106,108from the positioning device60, and with the predetermined positions of the light projector devices40and the imaging devices46relative to the positioning device60, to correlate detected rail edges in the rail reflection image data116with positions and orientations in three-dimensional space, and to generate120the geo-referenced rail geometry data122.

The positions of the rails20as continuous curves in three-dimensional space are then determined124from the geo-referenced rail geometry data122. In addition, one or more track profiles128are derived126from the rail geometry data122. In this example, the track profiles128are parameters relating to the geometry of the rails20as a function of position along the track section. Deriving126the track profiles128may include one or more of:

determining local track centerline points Yc from the rail geometry data122at or near the various locations Xi along the track18, and associating the set of determined track centerline points {Yc} with the system position data108to determine a first centerline profile130for the track18as a function of track distance;

determining local gauge values ΔYt between inner lateral edges24of the rails20at or near the various locations Xi along the track18, and associating the set of determined local rail gauge values {ΔYt} with the system position data108to determine a gauge profile132for the track18as a function of track distance;

determining local elevation values Zt for the track18at or near the various locations Xi along the track18, and associating the set of determined local elevation values {Zt} with the system position data108to determine an elevation profile134for the track18as a function of track distance, and

determining local cant values ΔZt (not shown) for the rails20at or near the various locations Xi along the track18, and associating the set of determined local cant values {ΔZt} with the system position data108to determine a cant profile136for the track18as a function of track distance;

In the exemplary method100shown inFIG.3, the acquisition140of three-dimensional point cloud data150includes acquisition144of three-dimensional laser ranging data146with point data corresponding to the two rails20and surroundings along the section of the track18, and generation148of the geo-referenced 3D point cloud data150by combining the set of 3D reflection data146with the orientation106and position data108.

In this example, the laser ranging data146is acquired144by scanning142one or more laser beams56across portions of the surroundings of the track20(e.g. by the rotating sources52of laser scanners50), while the system30is moved along the track18. Reflections of the laser beams56by the surroundings may then be detected (e.g. by the rotating detectors54of laser scanners50), to acquire144the laser ranging data146. The laser beams56may be formed by continuous wave radiation, or by a sequence of laser beam pulses.

In this example, acquisition144of ranging data146includes detecting reflections of the laser beams56that are reflected by points on nearby structures back towards the laser detector54, and computing distances between the laser sources52and the reflection points associated with each received reflection.

Generation148of the geo-referenced 3D point cloud data150includes combining a predetermined position of the laser scanners50relative to the positioning device60with the received system orientation data106and system position data108, and thereby associating each detected point in the laser ranging data146with a position in three-dimensional space.

Generation148of the geo-referenced 3D point cloud150may be executed in real time during the inspection run. The correspondences between, on the one hand, the orientation and position data106,108, and on the other hand, the detected points in the laser ranging data146, may be stored on the data storage device82during the inspection run. The stored data106,108,146may then be retrieved from the data storage device82after completion of the inspection run, and used in a post-processing stage to generate the geo-referenced 3D point cloud150.

The positions of the rails20as functions along the section of the track18are determined152from the geo-referenced 3D point cloud data150. An automated curve detection algorithm may assist determination152. Such a detection algorithm may be initialized based on knowledge of the rail positions determined124from the geo-referenced rail geometry data122.

In a further calculation step, a second track centerline profile156is derived154from the 3D point cloud data150. In this example, deriving154includes determining further track centerline points from the 3D point cloud data150at various locations along the track18, and associating the set of further track centerline points with the position data108, to generate the second centerline profile156for the track18as a function of track distance. Other track profiles relating to the geometry of the rails20as a function of position along the track section may be derived from the 3D point cloud data150, for example a second elevation profile.

The information of the track18and surroundings in the acquired geo-referenced data sets122,150may be compared160and/or combined, to allow track mapping with improved accuracy. In the example ofFIG.3, the track profiles128from the rail geometry data122may be compared160to the track profiles156from the 3D point cloud data150. For instance, the first centerline profile130may be compared to the second centerline profile156. Alternatively or in addition, the first elevation profile134from the rail geometry data122may be compared to the second elevation profile from the 3D point cloud data150.

Based on the comparison160, enhanced geo-referenced rail geometry data170and/or enhanced geo-referenced 3D point cloud data172is generated166. Data comparison160and generation166may be based on various metrics and correction methodologies. In further embodiments of the method, additional information from data sets acquired via supplementary inspection runs may be taken into account.

FIG.4illustrates that the method100may include inspection runs for different tracks. The mapping system30may for example be moved along each the adjacent tracks18,19shown inFIG.1. During each inspection run, the mapping system30is moved along one of the two tracks18,19. Each track18,19is traveled at least once during an inspection run dedicated to that particular track.

During at least one inspection run (the “first inspection run”), the system30is moved with the railway vehicle10along a section of the first track18. For this first inspection run, the acquisition of rail reflection image data116, geo-referenced rail geometry data122, etc., for the first track18proceeds as described above with reference toFIG.3. Positions of the rails20as functions along the section of the first track18are determined124from the geo-referenced rail geometry data122. One or more track profiles128are then derived126, among which a first centerline profile130for the first track18as a function of track distance.

During this first inspection run, the laser sources52of scanners50scan142the laser beams56across the first track18and its surroundings, and thereby scan across the second track19as well. The laser detectors54detect beam reflections from the rails20,21of both tracks18,19. The resulting geo-referenced 3D point cloud data150for this first inspection run thus includes point data corresponding to the rails20,21of both tracks18,19.

The positions of the rails20of the first track18and the rails21of the second track19are determined152in the geo-referenced 3D point cloud data150from the first inspection run. From this, a second track centerline profile156for the first track18and an adjacent centerline profile157for the second track19are derived154from the 3D point cloud data150.

During at least one other inspection run (the “second inspection run”), the system30is moved on and along the second track19. During this second inspection run, the system30may be coupled to the same railway vehicle10, which has been moved to the second track19prior to this inspection run. Alternatively, the system30may be coupled to another railway vehicle (not shown) that was already located on the second track19prior to the second inspection run. It should be understood that the terms “first inspection run” and “second inspection run” are used here only to distinguish the inspection runs, but should not be construed in a limiting manner by suggesting a particular ordering in time.

During the second inspection run, fan beams42are projected onto the second rails21, and the cameras46gather further rail reflection image data based on received reflections. During this second run, the positioning device60acquires orientation data107and position data109at various system positions along the second track19. Acquisition of further geo-referenced rail geometry data123, etc. proceeds analogous toFIG.3, but now for the second track19.

Positions of the rails20as functions along the section of the second track19are determined125from the further geo-referenced rail geometry data123. One or more further track profiles129are then derived127from the further geo-referenced rail geometry data123, among which is a further centerline profile131for the second track19as a function of track distance.

During this second run, the laser scanner50may also be operated to scan the surroundings of the second track19, and the first track18may also be covered by this scanning. This is, however, not required.

In this example, the step of comparing160includes calculating inter-track centerline distances ΔYc1between track centerline points on the first centerline profile130and track centerline points on the further centerline profile131. The calculated inter-track centerline distances ΔYc1are then expressed as a function of position along the tracks, to yield a first inter-track distance profile162.

The comparing160also includes calculating inter-track centerline distances ΔYc2between track centerline points on the second centerline profile156and track centerline points on the adjacent centerline profile157. The calculated inter-track centerline distances ΔYc2are also expressed as a function of position along the tracks, yielding a second inter-track distance profile164. Alternatively or in addition, the inter-track distance profiles162,164calculated in step160may include inter-track elevation differences between points in the elevation profiles obtained from the rail geometry data sets122,123and the 3D point cloud data150.

In this example, step166involves generation of enhanced geo-referenced rail geometry data170based on the comparison160of the first and second distance profiles162,164. The data enhancement166involves adjusting of geo-reference correspondences for the geo-referenced rail geometry data122and the further geo-referenced rail geometry data123, in order to let the first distance profile162converge towards the second distance profile164. This approach is based on the assumption that an accuracy of inter-track distances determined from one set of geo-referenced 3D point cloud data150of one single inspection run is significantly better than an accuracy of inter-track distances derived from geo-referenced rail geometry data sets122,123of two distinct inspection runs.

The adjusting of correspondences may involve spatial transformation of the orientation and position data106-109, to generate the enhanced rail geometry data170based on transformed orientation/position data and original rail reflection image data (i.e. without modifying the latter). The search for an optimal transformation of the system orientation and position data106-109may proceed in an iterative manner. The resulting transformation parameters may be smoothed as a function of position along the track, before being applied. This may reduce the likelihood of creating discontinuities in the enhanced rail geometry data170.

In alternative embodiments, spatial transformations may instead be applied to the original rail reflection data, to generate the enhanced geo-referenced rail geometry data170based on transformed rail reflection data and original orientation/position data (i.e. without modifying the latter).

Enhanced geo-referenced 3D point cloud data172may then be generated166, based on the transformed (and possibly smoothed) orientation/position data and the original 3D point cloud data150. This data enhancement166may further include searching for an optimal rigid body transformation (i.e. only rotations and translations) of the geo-reference correspondences for the 3D point cloud data150, in order to let the centerline profiles156,157in the 3D point cloud data150be spatially mapped onto the centerline profiles130,131in the enhanced rail geometry data170.

Optionally, the method may include generating174composite track data176by merging the enhanced geo-referenced rail geometry data170and the enhanced geo-referenced 3D point cloud data172into a single dataset. The resulting composite track data176may be used for display purposes.

The panoramic camera70may be used to acquire visual images of an area in front of the railway vehicle10during an inspection run. The known position of the panoramic camera70relative to the positioning device60and the acquired orientation and position data106,108may then be used to associate each image in the panoramic image data with a position in three-dimensional space, to generate a set of geo-referenced panoramic images at subsequent positions along the track. To facilitate track analyses, the system may allow an operator to select a specific position (or sequence of positions) along the track. The system may then retrieve a corresponding geo-referenced panoramic image and a corresponding portion of enhanced rail data170, enhanced 3D point cloud data172, or composite track data176, and add such data to the panoramic image (or image sequence) as an overlay.

FIG.5illustrates an alternative method200for mapping sections of railway tracks. Features and steps in the method200that have already been described above with reference to other method embodiments (and in particularFIGS.3-4) may also be present in the method200shown inFIG.5, and will not all be discussed here again. For the discussion with reference toFIG.5, like features are designated with similar reference numerals preceded by 200.

The left side ofFIG.5illustrates that in this example, the acquisition204jof orientation and position data206j,208j, and acquisition210jof geo-referenced rail geometry data222jare executed multiple times for the same track18. The labels j and k represent particular instances of a discrete index for generically indicating method steps and data associated with individual inspection runs. Repeated execution may be achieved via a sequence of individual inspection runs, or via simultaneous inspection runs by multiple systems30attached to the same railway vehicle10. During each inspection run, the railway vehicle10is moved along (at least) the same section of the track, so that this particular track section is covered in all inspection runs for this track.

This method200also includes generating220maveraged geo-referenced rail geometry data222mbased on spatially overlapping portions of the rail geometry data sets222j,222k. The label m is used to indicate an averaging step or its result. An average centerline profile230mis calculated in step226m. Other averaged track profiles may be generated as well, like an average cant profile, average gauge profile, and/or average elevation profile. The averaged track profiles may be calculated by averaging track profiles228j,228kderived for each rail geometry data set222j,222k.

The statistical spread of individual track profiles228j,228krelative to the associated average track profile228mmay be described in a point-wise manner, for example by calculating the local standard deviation (SD) from the local mean of the profile parameter as a function of position along the track section. In this way, a centerline SD profile σ1may be derived with respect to the average centerline profile230mfor the first track18. This centerline SD profile σ1provides a metric for the amount of spatial spread between centerline points from each the individual centerline profiles230jthat are assumed to correspond to the same position along the track. This spread (i.e. the SD) is determined for each position, and the set of such spread values along the track forms the centerline SD profile σ1.

The right side ofFIG.5illustrates that the procedure with multiple inspection runs and averaging for one track may be executed for an adjacent track19as well. Also for this track19, averaged geo-referenced rail geometry data223mand averaged track profiles229mare generated in steps221mand227mrespectively. A further averaged centerline profile231mand a further centerline SD profile σ2may also be derived for the second track19.

In this case, comparing step260includes calculating inter-track centerline distances ΔYc1between centerline points on the first average centerline profile230mand centerline points on the further average centerline profile231m, to derive a first inter-track distance profile262. The comparing260also includes calculating inter-track centerline distances ΔYc2between centerline points on the second centerline profile256and centerline points on the adjacent centerline profile257, to derive a second inter-track distance profile264.

In generation step266, the spread values per position from the centerline SD profiles σ1and σ2are used as weighting factors for the amount of adjustment that each of the geo-reference correspondences for the rail geometry data sets222mand223mshould receive. For instance, when the centerline SD profile σ1includes larger spread values for specific positions than the spread values for the corresponding positions from the further centerline SD profile σ2, then the adjustments of the geo-reference correspondences for averaged rail geometry data set222mis made larger at these positions than the adjustments for the averaged rail geometry data set223m. In alternative embodiments, the weighting factors may be based on other statistical dispersion characteristics for data sets222j,223jand/or228j,229j.

The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. It will be apparent to the person skilled in the art that alternative and equivalent embodiments of the invention can be conceived and reduced to practice. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Note that for reasons of conciseness, the reference numbers corresponding to similar elements in the various embodiments (e.g. method step210being similar to method step110) have been collectively indicated in the claims with only the lowest most significant digit. However, this does not suggest that the claim elements should be construed as referring only to features corresponding to those numbers. Although the similar reference numbers have been omitted from the claims, their applicability will be apparent from a comparison with the figures.

Other implementations of the disclosed inspection system may use alternative light sources that produce EM radiation with different wavelengths and/or angular spreads. Alternatively or in addition, 3D point cloud data may be acquired by imaging methods other than laser light detection and ranging-based techniques. For example, the 3D point cloud data may be derived from dense image matching techniques applied to a set of two-dimensional images acquired at successive positions along the track.

In the above-mentioned exemplary embodiment, the inspection system was mountable on a front side or rear side of a train. In alternative embodiments, the system may be mounted elsewhere onto a railway vehicle, in order to maintain the inspection system in a proper position with respect to the track.

Those skilled in the art and informed by the teachings herein will realize that the disclosed system and method can be used in other areas, such as on trams, in subways, or other vehicles or movable structures that travel along a fixed track with rails.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, wherein the technical effect is to provide a system for inspecting and/or mapping a railway track. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

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