Slope stability LiDAR

A Slope Stability Lidar that directs a beam of optical radiation into an area on a point by point basis, each point having an elevation and azimuth and a processor that acquires data and processes the data to compile direction data, range data and amplitude data for each point, segments the acquired data into blocks of data defining a voxel, averaging the acquired range data within the voxel to produce a precise voxel range value for each voxel, comparing voxel range values over time to identify movement and generating an alert if movement exceeds a threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Phase of the PCT application number PCT/AU2016/050953, filed on Oct. 12, 2016, which claims priority to, and all the benefits of, Australian Patent Application No. 2015904141 filed on Oct. 12, 2015 both of which are hereby incorporated in its entirety by reference.

FIELD OF THE INVENTION

The present invention relates to a method and apparatus for monitoring slope deformation using laser ranging. The invention finds particular application in open cut and underground mining applications.

BACKGROUND OF THE INVENTION

The Applicant has previously described a Slope Monitoring System based on using interferometric radar measurements to detect movement of an observed slope. The technique is well described in our International Patent Application number WO2002/046790. In one important application the Slope Stability Radar (SSR) described in WO2002/046790 is used to monitor rock walls in an open cut mine to detect any dangerous movement that may lead to slope failure.

The Applicant's SSR devices have proven to be very effective and have enjoyed significant commercial success. The content of WO2002/046790 is incorporated herein by reference in its entirety.

In WO2002/046790 reference is made to the use of laser EDM (Electronic Distance Measurement) as a tool that was used at that time to measure the dilation of cracks appearing on the crest or face of the rock slope. Most of the laser-based techniques monitor points or lines on a wall rather than an area of the wall face. This has meant that laser-based distance measurement systems have not been useful for deformation monitoring in mining applications, or similar situations.

For instance, Hu Hui has published a detailed doctoral thesis from Aachen University, Germany titled, “Deformation monitoring and modelling based on LiDAR data for slope stability assessment”. As recognised by Hui on page 17, “The challenge is how to efficiently analyse the LiDAR data and how to extract the valuable information (e.g. deformation signs) from huge amounts of data”. Hui does not present a solution except to apply greater processing power or limit the scan to reduce the amount of data. In spite of the best efforts of Hui the sub-millimetre precision required for slope monitoring is not demonstrated.

Reference may also be had to a useful review paper published online in 2010 and in Natural Hazards (2012) 61:5-28 with the title “Use of LIDAR in landslide investigations: a review”. The paper provides a useful discussion of LIDAR and is incorporated herein by reference.

In the conclusion section the authors make a number of relevant points about the limitation of LIDAR in slope stability monitoring including, “In a few years, LIDAR sensors will probably be a standard tool for landslide analysis . . . . As the technique is also progressing, more accurate and precise ALS (airborne laser scanner) and TLS (terrestrial laser scanner) devices will appear, allowing for the generation of more accurate DEM (digital elevation map) . . . . Nevertheless, the huge amount of data will remain a problem as the computers will need to be more powerful with increasing data acquisition capacity as it is already the case with mobile LIDAR systems that have an acquisition rate of up to 200 kHz. The real challenge is to develop new methods to better take benefit from HRDEM (high resolution digital elevation map). Indeed a lot of new information can be extracted from such DEM that we not yet contrived to do. Although great advances have been developed in geometrical aspects, most of the conceptual models remain tied to the past”. These comments are still applicable today.

SUMMARY OF THE INVENTION

In one form, although it need not be the only or indeed the broadest form, the invention resides in a Slope Stability Lidar comprising:

a laser producing a beam of optical radiation;

a scanner that directs the beam of optical radiation into an area on a point by point basis, each point having an elevation and azimuth with respect to the laser;

a detector that receives reflected optical radiation from each point;

a processor programmed to:acquire data from the detector and process the data to compile direction data, range data and amplitude data for each point;segment the acquired data into blocks of data defining a voxel;average the acquired range data within the voxel to produce a precise voxel range value for each voxel;compare voxel range values over time to identify movement; andgenerate an alert if movement exceeds a threshold.

The processor may also be programmed to average the acquired amplitude data to produce an average amplitude value for each voxel. The average amplitude value for each voxel may be used by the processor to generate a display of an image of the area. The processor may also display the identified movement on the image of the area.

The alert may be audible, visual or tactile. A visual alert may suitably be displayed on the display.

Suitably the processor of the Slope Stability Lidar may co-register the image of the area with a photograph or video of the area. The Slope Stability Lidar suitably comprises a camera that records the photograph or video.

The laser is preferably a pulsed laser and the processor determines range to a point using a time of flight calculation.

The scanner suitably scans the laser beam in azimuth using a rotating base and in elevation using a rotating mirror.

The location of each point may be determined in Cartesian coordinates (x,y,z) or polar coordinates (r,θ,ϕ). Each block forming a voxel may be selected based on a specific voxel size, such as all range points within an azimuth by elevation of 0.5 degrees by 0.5 degrees. The voxel size may have an azimuth of from 0.1 degrees to 1.0 degrees. The elevation may be 0.1 degrees to 1.0 degrees. The voxel size may be any combination of azimuth by elevation from 0.1 degrees×0.1 degrees to 1.0 degrees by 1.0 degrees. Some examples include 0.2 degrees by 0.3 degrees, 0.5 degrees by 0.4 degrees, or any other suitable size.

The voxel size may also have range limits rather than averaging all ranges within an elevation by azimuth. Range limits may be applied to exclude ranges that have known artefacts, for example a road.

In a further form the invention resides in a method of monitoring slope movement including the steps of:

directing a beam of optical radiation into an area;

scanning the beam of optical radiation in elevation and azimuth so as to cover the area on a point by point basis;

detecting radiation reflected from each point;

acquiring data from the detector and processing the data to compile direction data, range data and amplitude data for each point;

segmenting the acquired data into blocks of data defining a voxel;

averaging the acquired range data within the voxel to produce a precise range value;

comparing voxel range values over time to identify movement; and

generating an alert if movement exceeds a threshold.

The method may further include the step of averaging the acquired amplitude data to produce an average amplitude value for each voxel. The average amplitude value for each voxel may be used in a further step of the method to generate a display of an image of the area. The identified movement may suitably be displayed on the image of the area.

The display may be co-registered with a photographic or video image of the area.

DETAILED DESCRIPTION OF INVENTION

Embodiments of the present invention reside primarily in a Slope Stability Lidar apparatus that utilizes a laser to monitor the movement of a slope, wall or other region which may be subject to movement. Accordingly, the elements of the device have been illustrated in concise schematic form in the drawings, showing only those specific details that are necessary for understanding the embodiments of the present invention, but so as not to obscure the disclosure with excessive detail that will be readily apparent to those of ordinary skill in the art having the benefit of the present description.

In this specification, adjectives such as first and second, left and right, and the like may be used solely to distinguish one element or action from another element or action without necessarily requiring or implying any actual such relationship or order. Words such as “comprises” or “includes” are intended to define a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed, including elements that are inherent to such a process, method, article, or apparatus.

Referring toFIG. 1there is shown a Slope Stability Lidar10that is viewing a scene11that may have regions which are stable and other regions which are moving. In the embodiment ofFIG. 1the scene11is part of the slope of an open cut mine12, but as explained below the invention is not limited to this application. The Lidar10may be a commercially available device such as a Leica Scanstation P20 available from Leica Geosystems or it may be a proprietary device. The elements of the Lidar10are shown schematically in the block diagram ofFIG. 2. The Lidar10is constructed from a pulsed laser101, scanner102, receiver103, processor104and various output options105.

The Lidar produces a “point cloud” of a scene by scanning the laser in azimuth and elevation. The point cloud is commonly displayed as a colour-coded intensity or amplitude map that displays the amplitude (or intensity) of the reflected laser light for each azimuth, elevation and range point. The laser repetitively sends out pulses and detects each reflected pulse. The time between sending the pulse and receiving the reflection is used to calculate distance from the laser using time of flight. The combination of azimuth, elevation and distance is used by the processor to construct a three dimensional (3D) image of a scene around the Lidar. Although it is possible to generate the scene in a complete dome around the Lidar it is the case in most applications that only a small section of the dome is of interest.

Scanning of the laser may be achieved by any appropriate technique but it has been found that a spinning or oscillating mirror is effective for providing the elevation scan and a rotating base is effective for the azimuth scan.

The output105may be an image of the scene but the point data may also be accessed directly for further or alternate processing. Terrestrial Lidar scanners use all data that is collected to generate the scene. They produce a high quality image, which is to say that the range and direction measurements have high resolution, but a relatively long time is required to generate the image and a large data file is produced. The inventors have realized that the timescale to generate a data file and to process the data file to produce a scene using Lidar is too long to be useful for monitoring a region, such as a slope or a wall, for movement. The inventors have therefore developed a new processing approach that achieves useful movement data in a much shorter time and with much greater range precision than is conventionally available using Lidar.

The inventors have also found that the precision of laser scanning is not sufficient for movement detection in many applications, particularly safety applications such as monitoring rock/slope movement in a mine. The laser measurement precision is affected by range, colour of the target, sensor settings and environment. It is not unusual to measure movement noise of up to +/−8 mm for a stationary target. The process developed by the inventors increases the precision significantly so that Lidar can be used for rock/slope movement detection.

Referring toFIG. 3, there is shown a flowchart summarizing the steps for producing movement data using Slope Stability Lidar. The point cloud data is collected in the usual manner but an averaging method is used to calculate a value for average amplitude (or intensity) at a precise range measured in a segment of the region. The size of the segment is selected as a suitable trade off against resolution to improve precision. A typical segment will have an extent of 0.5 degrees×0.5 degrees and contain from several hundred to several thousand points from the point cloud. The selected segment is suitably circular but other shapes may also be selected such as a rectangle, triangle, hexagon or polygon.

The averaging process may be a simple average of the amplitude (or intensity) and range of all points within the selected segment. Alternatively the average may be a more complicated averaging process, for example by underweighting the values towards the periphery of the selected segment compared to the values towards the center of the segment.

The segments may be contiguous or may overlap by a determined amount. In most cases it will not be appropriate to omit points by leaving a space between the selected segments.

By way of example, the Leica ScanStation P20 (mentioned above) produces high density and high resolution point cloud data that can be advantageously used to increase the range precision of the scanner by applying a spatial averaging algorithm which trades-off high angular resolution for range precision. The spatial averaging algorithm can be described in three steps:1. Spherical parameterization of scanner coordinates;2. Segmentation of data evenly in the azimuth and elevation direction (with coarse range bins);3. Applying a weighted average for each segment with respect to the true centre value.

An optional fourth step is to display the data in a pixel that may be smaller than the averaged area. The pixel is most suitably square but could be hexagonal, circular or some other appropriate shape.

Spherical Parameterization

The raw data exported from the laser scanner represents each point in the Cartesian coordinate system. This system splits the 3D world into three planes (x, y, and z) where the origin (0, 0, 0) is represented as the scanner origin, as depicted inFIG. 4.

Segmentation is the process of dividing the data into smaller but meaningful parts termed segments, as depicted inFIG. 5. The point cloud retrieved from the laser scanner is converted to a standard file format that contains a single header line representing the number of points in the cloud followed by x, y, z and intensity values for each point separated by a new line. An example is shown below:

The first line of this file is the number of points in the point cloud. The second line is read as an x position of 1.997177, a y position of −20.065475, a z position of −0.973618 and an intensity of 342. This is converted to range of 20.18, azimuth of −84.31 degrees and elevation of −13.71 degrees. It should be noted that the P20 generates intensity values in the range −2047 to +2048. These are routinely scaled to a range of 0 to 100.

The data is then segmented into unique regular bins defined by equal azimuth and elevation angles. The segments may be saved into individual files for pixel point processing to help manage physical memory usage.

Weighted Average

A weighted average is expressed mathematically as:

wherexis the computed mean, w_n is the weight for data point n, and x_n is the value of data point n.

The process involves first computing the centrepoint of each segment to be averaged. The centrepoint is chosen as the true halfway point in both azimuth and elevation. Then a linear weighted average is applied in terms of distance from the centrepoint in terms of spherical coordinates as depicted inFIG. 6.

In the case of the point cloud generated by the laser scanner, the process is especially advantageous as it smoothes out any large deviations such as stray points that can be present due to edge effects or beam splitting.

The invention is not limited to the specific linear weighting shown inFIG. 6. Other weighting functions such as those depicted inFIG. 7could also be used. The top hat ofFIG. 7(a)weights all points equally. The normal distribution ofFIG. 7(b)places greater weight on the points towards the centre of the segment. The averaging window ofFIG. 7(c)applies a linear weighing to minimise edge effects andFIG. 7(d)also minimises edge effects but using a non-linear weighting.

As mentioned above, the segments may be contiguous as shown inFIG. 8(a). In this embodiment all points are used in the averaging process. An alternative arrangement is shown inFIG. 8(b)in which some points are used in more than one segment. The segments are overlapping. The weight function can also be applied to the range direction of the voxels.

The benefit of the process described above is clearly demonstrated inFIG. 9.FIG. 9shows the precision of measurements of range to a black card taken by a Leica Scanstation P20 at a range of 5 m. As can be seen, the averaged measurement120at 5 m is far more precise than the non-averaged measurement121. In various tests the inventors have found that for stationary rough rock wall targets precision of +/−0.09 mm per scan was achieved with this technique compared with as much as +/−17.2 mm per scan when comparing unprocessed point cloud data. In ideal laboratory conditions for moving targets the averaged data measured readings to an accuracy of ±0.05 mm as the movement occurred compared with ideal point cloud data of the target measuring movement with an accuracy of +/−8 mm.

By way of example, an embodiment of the invention was used to monitor a slope in a pit that included moving and stationary regions within an area. The slope is shown inFIG. 10and the monitored region is shown by the black box. The Slope Stability Lidar collected data from the region and the data was segmented into 0.5 by 0.5 degree voxels and the amplitude and range was averaged using the weightings ofFIG. 6and overlapping segments likeFIG. 8(b). The movement (in millimetres) in each voxel is displayed inFIG. 11using the colour palette scale to the right. The X-axis spans an azimuth range of 90 voxels (45 degrees) and the Y-axis spans an elevation range of 25 voxels (12.5 degrees). Movement towards the scanner is shown using the red end of the spectrum and movement away from the scanner is depicted using the blue end of the spectrum. Voxels with little or no movement are white or pale.FIG. 12shows the 3D photographic point cloud generated by the Leice Scanstation P20 with the movement scan ofFIG. 11shown below using the same coordinate system. The movement depicted inFIG. 12compares well with movement data collected using the Slope Stability Radar described in WO2002/046790.

The invention is not limited to use in open pits. Due to the compact nature of the Slope Stability Lidar and the intrinsic safety it can be used in underground mines. A standalone SSL pack130is shown inFIG. 13. The standalone SSL130comprises a combined Lidar and camera unit131that is mounted on a gimbal scanner132that oscillates in azimuth and elevation. A user interface and output133is provided directly to the processor134, which has an internal power supply (not visible). The SSL130may conveniently be mounted on a stable wall (although this is not essential) of an underground mine and configured to monitor a nearby wall or roof for movement, as shown inFIG. 14. Mounting points140may be conveniently located so that a rock mechanic141can move the standalone SSL130from place to place as needed.

In another embodiment the SSL is deployed at the end of a boom to monitor a slope, as depicted inFIG. 15. A Lidar assembly150comprises a boom151and a tripod mount152. The tripod mount152is positioned at the end of an access drive153. A bore hole154is drilled from the access drive153into a slope cavity155and the Lidar & camera unit156is lowered on the boom151to monitor the top of the slope cavity155above the ore157. The Lidar and camera unit156has the same elements as the standalone SSL130. An alternate deployment could be from the gallery158.

FIG. 16shows an enlarged view of the SSL in the application ofFIG. 15. The thickness of the roof of the slope is shown thinner than reality for easier description. It can be seen that the bore hole154is cased with rollers163to stabilise the boom151and remove movement.

The tripod mount152is shown in more detail inFIG. 17including the user interface171for use by the worker141. The user interface170and processor allows the worker141to control azimuth and elevation of the Lidar scan and to observe a local display. A communication module171provides an uplink to communicate with a mine control room. It also may provide a local alarm to give warning of dangerous movement.

The Lidar unit156need not be located at the end of the boom151but could be located at the tripod mount152with the laser beam delivered through the boom151using suitable optics. This embodiment removes all sources of spark from the slope and is intrinsically safe. The same principal may be applied for other underground applications with any source of spark being located well away from potential danger and the laser beam being conveyed to the monitor site by optics, such as optical fibre.

Although various pixel shapes are disclosed above, the preferred pixel shape is square. An effective manner of producing square pixels using the method described herein is shown inFIG. 18. Although the point cloud data is captured from a dome centred on the Lidar, it can be rolled out to a grid of azimuth and elevation as shown inFIG. 18. The size of each square of the grid in the example ofFIG. 18is 0.5 degrees by 0.5 degrees. The weighting factors181,182are applied to the data in the manner depicted inFIG. 18and described above to produce a square pixel.

The processing shown inFIG. 18is applied to produce the display ofFIG. 19. The display format is familiar to users because it is the same as the display presented for the SSR product mentioned earlier.FIG. 19shows a visual image of a monitored region with an amplitude map co-registered below.FIG. 20shows the same scene but with movement data rather than amplitude data.

An alternate spatial averaging process is depicted inFIG. 21. Raw data is acquired as described previously and converted to a spherical coordinate system. The data is segmented into voxels of a selected size; say 1°×1°. The voxel is over-sampled by collecting data from the neighbouring voxels to produce a block of 9 voxels measuring 3°×3°. The block is divided into 900 0.1°×0.1° sub-voxels. The mean range for each sub-voxel is determined using a weighted average calculation as described previously. The mean range for each sub-voxel is compared with the mean range for that sub-voxel calculated from the previous scan. The median value for the 900 sub-voxels is determined and this value is displayed as the movement value for the selected 1°×1° voxel. The process is repeated for each 1°×1° voxel to generate a movement map.

As an optional variation the median movement value may be determined from a subset of the 900 sub-voxels, for instance by applying a threshold. The threshold may be set to exclude any range change outlier. That is to say, if the range change exceeds a reasonable limit it is excluded. This will typically be some number of standard deviations from the mean value.

For regions of low grazing angle between the area being monitored and the Slope Stability Lidar, a small Lidar angular beam pointing error can result in a large change in range value. Also, low grazing angles provide less back scatter and therefore lower signal to noise ratio, which means higher error in the range measurements. This can lead to large errors in deformation monitoring. To deal with this error a method has been developed as shown inFIG. 22. Raw data is acquired and converted to a spherical coordinate system. The data is segmented into voxels of a selected size; say 1°×1°. The next step is to describe a plane of best fit for the point cloud data inside each voxel. Then an angle is determined between the plane of best fit and a line of sight (LOS) vector to the scanner from the centre of the voxel. The angle is measured against a threshold. If the angle is above the threshold the process ofFIG. 21is followed. If the angle is low (grazing angle) the range value from the LOS vector is replace with a range value from the normal vector to the plane of best fit, and then the process ofFIG. 21is followed. The plane of best fit could be extended to use more of the point cloud around the voxel. This would make the plane less sensitive to wall roughness.

In the domain of geotechnical engineering in a mining context, the behaviour of the rock surface (otherwise known as the coherence of the surface) is important to detect and measure. A rock surface that cracks or breaks up or erodes can give vital clues to impending collapse, while conversely, in the centre of a moving block of rock coherence may be high while the entire slab moves compared with the incoherent edges of the block that grind away and splinter as the rock slab moves. At other times the coherence of a target may be affected by vegetation, passing people, machines or vehicles, or by water or other objects moving on the surface, meaning that a measure of coherence of the surface can at times be used as a data quality indicator. The Applicant has previously described the use of coherence with slope stability radar in their earlier international publication WO/2007/009175, the content of which is incorporated herein by reference.

The inventors have determined that a spatial coherence metric for each voxel generated by the SSL method can be calculated based on changes in the spatial distribution of the point cloud range measurements for each voxel and its surrounding neighbours between scans (or over time). Such a metric can be shown on a scale of 1 to 0, where 1 is 100% coherent and 0 is 0% coherent. The coherence metric describes how the small scale spatial character of the rock wall scattering surface changes between scans. In comparison, deformation describes a larger scale or voxel size change in the bulk range to the rock wall surface. Either range or amplitude distributions, or the combination of both, can be used to determine the coherence metric. The coherence metric can then be generated as an image for the area.

The inventors have found for this geotechnical application there is generally too much noise in raw Lidar point cloud data for a useful coherence metric and some level of point cloud averaging is required.

In one embodiment of the invention, raw data is acquired as described previously and converted to a spherical coordinate system. The data is segmented into voxels of a selected size; say 1°×1°. The voxel is over-sampled by collecting data from the neighbouring voxels to produce a block of 9 voxels measuring 3°×3°. The block is divided into 900 0.1°×0.1° sub-voxels. The mean range and mean amplitude for each sub-voxel is determined using a weighted average calculation as described previously. The sub-voxel spatial distributions are compared with the previous scan to determine the coherence for the given voxel. The value is displayed as the coherence value for the selected 1°×1° voxel typically on a scale of 1 to 0 where 1 is 100% coherent and 0 is 0% coherent. The process is repeated for each 1°×1° voxel to generate a coherence image or map with a colour gradient applied to the spread of values.

A Delta Coherence image or map can be generated from this coherence data if scanning continues over a longer time period, for example, three to tens-of-thousands of scans. To generate such an image or value the above process is repeated many times over many scans, and the lowest coherence metric (ie., minimum hold function) for each 1o×1o voxel is displayed over the selected time period as the delta coherence value for that voxel. Coherence maps may be displayed in the form described in our earlier international application mentioned above.

The above example is purely for the purpose of explanation. The invention is not limited to voxels of the specific size mentioned but may be in the ranges mentioned earlier. The number of neighbouring voxels may also be varied. For instance, voxels may be circular or hexagonal and therefore have 6 near neighbours and 8 corner neighbours.