Patent Description:
During operation of an aircraft, it may be desirable for a pilot to receive feedback relating to a status of the aircraft or components thereof. This may allow the pilot to make decisions and/or take actions dependent on the status. <CIT> relates to a method and apparatus for capturing and processing images from a portion of a system in order to detect a warning condition for providing warnings to users or operators of the system. <CIT> relates to apparatus and associated methods for aligning a taxi-assist camera such that each image frame of real-time video that the camera generates has a standard presentation format. <CIT> relates to an estimated route presentation apparatus. An estimated route is obtained on the basis of information extracted from an image captured an onboard camera without acquiring detection information, such as steering angle, from an onboard apparatus. <CIT> relates to a method for predicting a change in a direction of travel of a vehicle using a camera image of a steerable wheel of the vehicle, and comparison of the recorded camera image with several reference image whereby an associated wheel angle is stored for every reference image as part of a training phase.

According to a first aspect of the present invention, there is provided a computer-implemented method of determining a steering angle of an aircraft landing gear, wherein the steering angle is adjusted for turning an aircraft during movement on the ground, the method comprising: obtaining an input image of the aircraft landing gear, wherein the input image is captured by an imaging device positioned on a strut of the aircraft landing gear so as to be directly above a tyre of the aircraft landing gear when the landing gear is extended; comparing the input image against a plurality of reference images, the plurality of reference images comprising images of the aircraft landing gear at known steering angles; determining a most similar reference image, the most similar reference image comprising a reference image of the plurality of reference images most closely matched to the input image; and determining, based at least in part on the most similar reference image, the steering angle of the aircraft landing gear.

The steering angle or information related to the steering angle may be fed back to a pilot to assist in manoeuvring an aircraft. For example, when taxiing, the pilot may be informed of the steering angle of the landing gear so as to know how the aircraft is going to move, and/or so as to know whether the landing gear is responding appropriately to a steering command. Determining the steering angle of an aircraft landing using image comparison may enable the steering angle to be determined without any physical interaction with the aircraft landing gear.

Optionally, the method comprises obtaining the input image of the aircraft landing gear using an imaging device. In this way, the steering angle of the aircraft landing gear may be determined in a non-intrusive manner.

Optionally, the at least one imaging device may be used for other purposes in addition to determining the steering angle. For example, the imaging device may be used for determining a rate of extension and/or retraction of the landing gear, and/or for monitoring the landing gear bay for the presence of foreign objects. This may reduce the total number of sensors/devices within an aircraft landing gear or aircraft landing gear bay.

Optionally, determining the most similar reference image comprises utilising a machine learning algorithm.

Optionally, the determining the most similar reference image comprises calculating respective similarity metrics between the input image and each of the plurality of reference images and comparing the similarity metrics to a threshold to determine the one closest to a threshold value indicative of highest degree of similarity.

Optionally, comparing the input image against the plurality of reference images comprises comparing input image pixel values against corresponding reference image pixel values of each of the plurality of reference images.

Optionally, the input image pixel value and the reference image pixel value comprise information indicative of a colour of the input image and the reference image respectively. For example, the pixel values of the input image and the reference image may comprise a red, green and blue component value. When comparing the input image against the reference image, the values and/or relative values of the red, green and/or blue component values may be compared. The reference image which has the most pixels with red, green and/or blue component values in common to the input image may be the most similar reference image.

Optionally, the plurality of reference images comprises a series of images of the aircraft landing gear having between <NUM>° and <NUM>° intervals of steering angle between each image. Optionally, the intervals are between <NUM>° and <NUM>°, <NUM>° and <NUM>°, <NUM>° and <NUM>°, <NUM>° and <NUM>°, or <NUM>° and <NUM>°. This may provide good resolution for determining the steering angle which may help to ensure accurate steering angle determination.

Optionally, the plurality of reference images cover a total range of steering angles of the aircraft landing gear of between <NUM>° and <NUM>°. Optionally, the total range is between <NUM>° and <NUM>° or between <NUM>° and <NUM>°. For example, the range of steering angles may be from -<NUM>° to +<NUM>° relative to neutral, i.e. relative to when the landing gear steering angle is at <NUM>° and enables the aircraft to travel in a straight line along the ground. This may help to ensure that the full range of steering angles of the landing gear is covered.

Optionally, comparing the input image pixel values against the corresponding reference image pixel values comprises calculating the root mean square deviation (RMSD) between the input image pixel values and the corresponding reference image pixel values.

Optionally, determining the most similar reference image comprises determining the reference image with the smallest root mean square deviation between the input image pixel value and the corresponding reference image pixel value. This may help to provide a quantitative measure of the similarity between input image and the reference images which may make it easier and/or quicker to determine the most similar reference image.

Optionally, comparing the input image pixel values against the corresponding reference image pixel values comprises transforming the input image pixel values into a one-dimensional input image vector, transforming the reference image pixel values into a one-dimensional reference image vector, and determining a cosine similarity between the input image vector and the reference image vector. The cosine similarity may provide emphasis on the structure of the input image and the reference image and may be less prone to differences caused by environmental variations. This may improve the performance and accuracy of the method.

Optionally, the method comprises determining a subset of the plurality of reference images to be compared against the input image and comparing the input image against the subset.

Optionally, the input image is only compared against the subset of the plurality of images. The subset may reduce the number of reference images needed for comparison, which may increase the efficiency of the method.

Optionally, the determining the subset comprises determining the subset based on a previous determination of the steering angle of the aircraft landing gear. For example, the method may determine the subset of reference images with a steering angle close to a previously determined steering angle, as the aircraft landing gear may be expected to be close to the previously determined steering angle.

Optionally, the method comprises determining a region of interest of the image, the region of interest comprising part of the image in which a component of interest of the aircraft landing gear is expected to be located in normal operation, wherein the comparing the input image against the plurality of reference images comprises comparing the region of interest against the plurality of reference images.

Optionally, the component of interest is a component of the aircraft landing gear which moves as the steering angle of the aircraft landing gear changes. As such, the position of the component of interest may be indicative of the steering angle of the aircraft landing gear. Optionally, the component of interest is a tyre. Optionally, the component of interest is a torque link.

Optionally, the method comprises processing the input image to remove background noise from the input image. For example, the method may comprise blurring the input image, such as by applying a smoothing filter to the input image. This may help to prevent background noise undesirably influencing the determination of the steering angle, which may increase the accuracy of the method.

Optionally, the method comprises applying a filter to the input image to greyscale the image. Converting the image to greyscale may help with subsequent image processing steps. For example, a grayscale image allows for the creation of a binary image which can then be used for subsequent image analysis.

Optionally, the filter is applied to greyscale the image before the input image is compared against the reference images.

Optionally, the method comprises performing gamma correction on the input image.

Optionally, the gamma correction is performed before the input image is compared against the reference images. Gamma correction may help to ensure consistency of gamma levels between the input image and the plurality of reference images.

Optionally, the method comprises processing the input image and the plurality of reference images by applying an imaging process to the input image and applying the same imaging process to the plurality of reference images. This may help to reduce variations caused by the external environment, such as lighting conditions, and may improve comparisons between the input image and the plurality of reference images.

Optionally, processing the input image and the plurality of reference images comprises determining a mask to be applied to the input image and the plurality of reference images and applying the mask to the input image and the plurality of reference images. The mask may cover pixels within the input image and the plurality of reference images which are not of interest for determining the steering angle, such as pixels corresponding to the background of the image, such that those pixels may be excluded from the comparison between the input image and the plurality of background images. Determining the mask may comprise comparing the reference images in the plurality of images against each other to determine a difference between corresponding pixels in the plurality of reference images, determining whether the difference is less than a threshold value, and forming the mask from pixels with the difference less than the threshold value. If the difference between pixels is less than the threshold value, this may indicate that there is no change in the pixel between the reference images and the pixel is therefore part of the background (or not of interest for determining the steering angle). The threshold value may be user defined. Masked areas of the images may be morphed and combined to smoothen the mask. The mask may block out the background which is not relevant to the determination of the steering angle. This may increase the speed of the method as fewer pixels within the respective images need to be compared.

Optionally, processing the input image and the plurality of reference images comprises determining a histogram of the input image and a histogram of the plurality of reference images. Optionally, processing the input image and plurality of reference images comprises modifying the input image such that the histogram of the input image matches the histogram of the plurality of reference images. This may further harmonise the input image with the plurality of reference images to improve comparison between the input image and the plurality of reference images. Optionally, processing the input image and plurality of reference images comprises modifying the input image and the plurality of reference images such that respective histograms of the input image and the plurality of reference images match a predefined histogram. Optionally, processing the input image and the plurality of reference images comprises using contrast limited adaptive histogram matching to match the respective histograms of the input image and the plurality of reference images. This may harmonise the input image and the plurality of reference images to improve the comparison between the input image and the plurality of reference images.

Optionally, processing the input image and the plurality of reference images comprises defining a steering region in the input image and the plurality of reference images, the steering region comprising a subsection of the input image and the plurality of reference images that changes when the steering angle of the aircraft landing gear changes, and cropping the input image and the plurality of reference images to the steering region, wherein comparing the input image against the plurality of reference images comprises comparing the steering region of the input image against the steering region of the plurality of reference images. Cropping the input image and the plurality of reference images to the steering region may remove background from the input image and the plurality of reference images, reducing the size of the images to be compared and therefore increasing the speed and/or efficiency of the method.

Optionally, processing the input image and the plurality of reference images comprises blurring the input image and the plurality of reference images. This may smoothen the input image and the plurality of reference images which may improve the performance of the method. Blurring may comprise averaging, Gaussian blurring, median blurring and/or bilateral filtering.

Optionally, processing the input image and the plurality of reference images comprises sharpening the input image and the plurality of reference images. The method may comprise applying a sharpening kernel to the input image to sharpen the input image. This may sharpen edges in the input image to emphasise features in the input image.

Optionally, processing the input image and the plurality of reference images comprises denoising the input image and the plurality of reference images. This may reduce noise within the image to improve the performance of the method.

Optionally, the plurality of reference images is for a given aircraft type. Optionally, the reference images are stored in a memory of the aircraft. Optionally, the reference images are stored on a database accessible by the aircraft.

Optionally, the method comprises providing information indicative of the steering angle of the aircraft landing gear to a crew of an aircraft. Optionally, the method comprises displaying the steering angle of the aircraft landing gear in a cockpit of the aircraft. This may allow the pilot and/or other member of the crew to be quickly and easily informed of the steering angle of the landing gear of the aircraft. The pilot may be informed of the steering angle of the aircraft landing gear even if the aircraft landing gear is not in their direct sight. This may help the pilot with manoeuvring the aircraft on the ground. Moreover, the determined steering angle of the aircraft landing gear may be used to limit further movement/rotation of the aircraft landing gear. For example, if the steering angle of the aircraft landing gear is determined to be at a maximum safe steering angle, the pilot may be prevented from increasing the steering angle further. The pilot may also receive a warning or other feedback when the maximum steering angle is reached.

Optionally, the method comprises providing information indicative of the steering angle of the aircraft landing gear to the crew of the aircraft while the aircraft is not on the ground. This information may be used to determine whether the aircraft landing gear is in the correct position/orientation for landing and/or cruise. The aircraft may carry out a pre-land test in which the aircraft landing gear is actuated to turn in the landing gear bay to ensure that it is operating correctly before being extended. The method may comprise provide information indicative of the angle of the aircraft landing gear in the landing gear bay which can be used to confirm whether the aircraft landing gear is operating correctly. This may allow the crew to make an informed decision on whether it is safe to continue with landing and/or continued flight.

Optionally, the method comprises storing the determined steering angle. Optionally, the method comprises storing the determined steering angle on a memory of the aircraft. Optionally, the method comprises storing the determined steering angle on a database accessible by the aircraft.

Optionally, the plurality of references images is generated by capturing images of the aircraft landing gear while receiving information indicative of the steering angle of the aircraft landing gear, and saving the images and the corresponding steering angle to a memory.

Optionally, the information indicative of the steering angle of the aircraft landing gear may be determined by a sensor on the aircraft landing gear. For example, the information indicative of the steering angle may be determined by a rotary variable differential transformer (RVDT).

According to a second aspect of the present invention, there is provided an aircraft controller for determining a steering angle of an aircraft landing gear, wherein the steering angle is adjusted for turning an aircraft during movement on the ground, the aircraft controller configured to: obtain an input image of an aircraft landing gear captured by an imaging device, wherein the imaging device is positioned on a strut of the aircraft landing gear so as to be directly above a tyre of the aircraft landing gear when the landing gear is extended; compare the input image against a plurality of reference images, the plurality of reference images comprising images of the aircraft landing gear at known steering angles; determine a most similar reference image, the most similar reference image comprising the reference image of the plurality of reference images most closely matched to the input image; and determine, based at least in part on the most similar reference image, the steering angle of the aircraft landing gear.

According to a third aspect of the present invention, there is provided a system for determining a steering angle of an aircraft landing gear, the steering angle for turning an aircraft during movement on the ground, the system comprising: an imaging device located on a part of the aircraft which is in view of the aircraft landing gear; and the aircraft controller of the second aspect.

Optionally, the imaging device comprises a first imaging device and a second imaging device.

Optionally, the first imaging device and the second imaging device are different types of imaging device. This may help to increase the robustness of the system as the first and second imaging devices may comprise different failure conditions. For example, a situation that may cause the first imaging device to fail to operate correctly may not have the same effect on the second imaging device.

Optionally, the first imaging device comprises a lidar sensor and the second imaging device comprises a camera.

Optionally, the first imaging device comprises a first sensor and the second imaging device comprises a second sensor. Optionally, the first sensor is a camera sensor and the second sensor is a lidar sensor.

Providing different types of imaging device may allow for different types of data to be captured. For example, a lidar sensor may provide depth information of the image captured. The different types of data may then be used to determine other information about the aircraft landing gear. For example, the depth information from the lidar sensor may be used to determine the depth of the tread of the tyre, so as to determine when the tyre may need replacing.

According to a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing instructions that, when executed by an aircraft controller, cause the aircraft controller to carry out the method according to the first aspect of the present invention.

According to a fifth aspect of the present invention, there is provided an aircraft comprising the system according to the third aspect of the present invention.

<FIG> shows a schematic view of an aircraft <NUM> according to an example. The aircraft <NUM> comprises a nose landing gear <NUM> and two sets of main landing gear <NUM>. During movement of the aircraft <NUM> on the ground, the angle of the nose landing gear <NUM> can be adjusted to alter the trajectory of the aircraft <NUM> (i.e. to turn the aircraft <NUM>). In some examples, the angle of the main landing gear <NUM> is also adjusted to aid movement of the aircraft <NUM> on the ground. The aircraft <NUM> comprises a cockpit <NUM> from which a member of the flight crew, e.g. a pilot, controls the aircraft. The cockpit comprises an interface, e.g. a joystick or dial, to control the steering angle of the nose landing gear <NUM>.

<FIG> show a schematic front and side view of an aircraft landing gear <NUM> respectively. The aircraft landing gear <NUM> shown in <FIG> is the nose landing gear <NUM> of <FIG>. In other examples, the aircraft landing gear <NUM> is the main landing gear <NUM> of <FIG>. The aircraft landing gear <NUM> comprises two tyres <NUM>, a strut <NUM> and a torque link <NUM>. Each of the tyres <NUM> comprises a tread <NUM>. The aircraft landing gear <NUM> is configured to be retracted and stored in a landing gear bay during flight, and to be extended for take-off and landing.

Also shown schematically in <FIG> is a system <NUM> for determining the steering angle of the aircraft landing gear <NUM>. The system <NUM> comprises an imaging device <NUM> and an aircraft controller <NUM>. The imaging device <NUM> is provided on the strut <NUM> of the aircraft landing gear <NUM> and captures images of the aircraft landing gear <NUM>. The imaging device <NUM> is positioned on the strut <NUM> so as to be directly above the tyre <NUM> when the landing gear <NUM> is extended. In some examples which do not fall within the scope of the claimed invention, the imaging device <NUM> is located in another location, for example on another part of the aircraft landing gear <NUM> or on another part of the aircraft <NUM> which is in view of the aircraft landing gear <NUM>. The imaging device <NUM> shown in <FIG> comprises a camera. In other examples, the imaging device <NUM> may comprise another optical imaging device, e.g. a lidar sensor. In some examples, the imaging device <NUM> comprises both a camera and a lidar sensor. The aircraft controller <NUM> of the system <NUM> is configured to carry out any method of determining the steering angle of the aircraft landing gear <NUM> discussed herein.

<FIG> shows a flow chart of a method <NUM> of determining a steering angle of the aircraft landing gear <NUM>. The method <NUM> is performed by the aircraft controller <NUM> and comprises: obtaining <NUM> an image of the aircraft landing gear <NUM>; performing <NUM> edge detection on the image; determining <NUM>, based at least in part on an edge obtained by the edge detection, a relative position of a component of the aircraft landing gear <NUM>; and determining <NUM>, based at least in part on the relative position of the component, the steering angle of the aircraft landing gear <NUM>. <FIG> show schematic views of the image <NUM> obtained by the method <NUM>.

As shown in <FIG>, the image <NUM> includes a view of components of the aircraft landing gear <NUM>. The method <NUM> comprises determining a relative position of a component of the aircraft landing gear <NUM> which is used to determine the steering angle. In the image <NUM>, the component comprises one of the tyres <NUM> of the aircraft landing gear <NUM> and in particular the tread <NUM> of the tyre <NUM>. The image <NUM> shown in <FIG> is obtained from the camera <NUM> positioned on the strut <NUM> of the aircraft landing gear <NUM> as shown in <FIG>. The camera <NUM> is configured to capture a video of the aircraft landing gear <NUM> and a single frame of the video is obtained during the method <NUM> to determine the steering angle. This allows from the continual monitoring of the steering angle of the aircraft landing gear <NUM>. Although the image <NUM> in <FIG> is obtained from the camera <NUM>, in some examples the image <NUM> is obtained from a memory on the aircraft <NUM>, e.g. where the image <NUM> has been temporarily stored after previously being captured by the camera.

After the image <NUM> has been obtained by the aircraft controller <NUM>, the aircraft controller <NUM> performs the method <NUM> to perform <NUM> image pre-processing to the image <NUM>. As shown in <FIG>, the image pre-processing is applied before any subsequent image processing steps are performed. The image pre-processing includes applying a filter to the image <NUM> to greyscale the image <NUM>. To grayscale the image <NUM>, each pixel of the image <NUM> is converted to a shade of grey based on the brightness/luminance of the pixel. Converting the image to greyscale may help with subsequent image processing steps. For example, a grayscale image allows for the creation of a binary image (e.g. through image thresholding) which can then be used for subsequent image analysis.

Performing <NUM> image pre-processing to the image <NUM> also includes performing gamma correction on the image <NUM>. The gamma correction may help to correct the brightness level in the image <NUM> and may improve the subsequent image processing steps. Gamma correction may be applied to the image <NUM> using a non-linear transformation of the form: <MAT> where O is an output pixel value, I is an input pixel value from the image <NUM> and γ is the gamma correction value. When γ < <NUM> the original dark regions will be brighter and when γ > <NUM> the opposite occurs.

Following performing <NUM> the image pre-processing, the method <NUM> comprises determining <NUM> a region of disinterest in the image <NUM> and applying <NUM> a mask <NUM> to the image <NUM> to remove the region of disinterest from the image <NUM>. <FIG> shows a schematic view of the image <NUM> after the mask <NUM> has been applied. In the image <NUM> shown in <FIG>, the mask <NUM>, and therefore the region of disinterest, is indicated by the hatched lines. The region of disinterest includes part of the image <NUM> in which the tyres <NUM> are not expected to be present in normal use. For example, in the image <NUM> of <FIG>, the tyres <NUM> cannot be present in the location of the strut <NUM>. As such, this part of the image <NUM> is determined to be part of the area of disinterest and the mask <NUM> is applied to this area (as shown in <FIG>). As the tyres <NUM> are not expected to be in the area of disinterest during normal use, this area is excluded from further analysis which may reduce computational requirements and increase the efficiency of the method <NUM>.

The region of disinterest in <FIG> is determined by analysing a series of images of the aircraft landing gear <NUM> and determining which parts of the image do not vary significantly between sequential images. The series of images are a series of still images taken from the video of the aircraft landing gear <NUM> captured by the camera <NUM>. It is assumed that any parts of the image which do not vary significantly between sequential images contain components which are substantially static/not moving. As such, these parts of the image are considered less relevant to the determination of the steering angle of the aircraft landing gear <NUM> and are attributed to the region of disinterest.

To determine the region of disinterest it is determined whether a property of a pixel in one image varies by more than a threshold amount over the corresponding pixel in a subsequent or preceding image. The property comprises a pixel value which may include information such as the colour or intensity of the pixel. If the pixel does not vary by more than the threshold amount, that pixel is determined to be part of the region of disinterest. The threshold amount varies depending on conditions, such as lighting conditions, and can be empirically determined. For example, as the aircraft <NUM> travels over runway lights, this may affect the relative brightness of sequential images, meaning that a different threshold is required compared to a situation where the lighting conditions are constant. The threshold amount may also vary between different aircraft types. Although the region of disinterest is determined after the image <NUM> has been obtained in the method <NUM> of <FIG>, in some examples the region of disinterest is determined or known before the image <NUM> is obtained and the mask <NUM>, based on the region of disinterest, is applied after the image <NUM> is obtained.

Information indicative of the mask <NUM> and/or region of disinterest is stored on the memory of the aircraft <NUM> to enable such information to be used in subsequent steering angle determinations. This may allow for quick and easy access to the information, which may increase the efficiency and/or speed of the method <NUM>. In other examples, the information is stored remotely from the aircraft <NUM>, which may allow multiple aircraft <NUM> to access the information. This may also allow the information to be updated at a central location, rather than having to update the memory of each aircraft <NUM> individually. For example, the information may be updated if the configuration of the aircraft landing gear <NUM> is changed or if the position of the imaging device <NUM> is changed. In some examples, the mask <NUM> and/or region of disinterest are constant for a given aircraft type.

After performing <NUM> the image pre-processing and applying <NUM> the mask to the image, the method <NUM> comprises applying <NUM> a threshold to the image <NUM>. Thresholding is a type of image segmentation which changes the pixels of the image <NUM> to make the image <NUM> easier to analyse. By applying <NUM> the threshold to the greyscale image <NUM>, the greyscale image <NUM> is transformed into a binary image, i.e. a black and white image. The threshold applied to the image <NUM> replaces each pixel in the image with a black pixel if the intensity of the pixel is less than a threshold value and a white pixel if the intensity is greater than the threshold value. The threshold value is pre-determined and constant for the entire image <NUM>.

The threshold described above uses a pre-determined threshold value which is not dependent on the image <NUM> being analysed. In some examples, it is desirable to apply a tailored threshold to the image <NUM> which is better suited to the particular image <NUM>. Such a threshold comprises Otsu thresholding (also known as Otsu's binarization or Otsu's method). Otsu thresholding determines an optimal global threshold from an image histogram. All possible threshold values are iterated and the spread of pixel levels is determined, i.e. the number of pixels that are in the background vs the number of pixels that are in the foreground, with the aim of determining the threshold value which makes these two numbers equal/close to equal.

In some examples, it may be necessary to use a threshold that varies across the image <NUM>, i.e. is not a single constant value across the entire image <NUM>, to account for local lighting inconsistencies. In such examples, an adaptive threshold is used (also known as dynamic and/or local thresholding). Rather than using a single threshold value for the entire image <NUM>, adaptive thresholding calculates a threshold based on a small region around a pixel, which can lead to different threshold values across the image <NUM>. Examples of adaptive threshold which can be used include Mean or Gaussian thresholding. Mean thresholding takes the mean threshold of the pixels surrounding a given pixel, whereas in Gaussian thresholding the threshold value is the Gaussian weighted sum of neighbourhood values (i.e. of the pixels adjacent to the pixel being considered).

As well as determining the region of disinterest within the image <NUM> the method also comprises determining <NUM> a region of interest of the image <NUM>. The region of interest comprises part of the image <NUM> in which the tyres <NUM> are located. Determining <NUM> the region of interest comprises applying contour detection to the image <NUM>. The contour detected image <NUM> is shown in <FIG>. Contour detection enables the identification of borders of objects within an image. In <FIG>, the contour detection is performed to locate the tyres <NUM>, or a part thereof, within the image <NUM>. The largest contour <NUM> that is detected is assumed to be a contour of the tyres <NUM>. As shown in <FIG>, the region of interest <NUM> includes the contour <NUM> detected in <FIG>. Once the area of interest is determined, a smaller portion <NUM> of the region of interest <NUM> is selected. This helps to ensure that everything being considered is a part of the tyre <NUM> and is not a fringe section on the boundary of the tyre <NUM>. The contents of subsection <NUM> are shown in more detail in <FIG>.

Once the portion <NUM> of the region of interest is selected, the method comprises performing <NUM> edge detection on the image <NUM>. The edge detection is applied only to the portion <NUM> identified previously. Edge detection aims to identify edges within a digital image at which the image brightness changes sharply, i.e. where the image brightness is discontinuous. Edge detection processes can generally be grouped into two categories: search-based and zero-crossing based. In search-based methods, edges are detected by first computing a measure of the strength of the edges, e.g. using a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. In zero-crossing based methods, edges are found by searching for zero crossings (the point where the sign of a mathematical function changes, i.e. the point at which the mathematical function crosses an axis) in a second-order derivative expression, such as the Laplacian, computed from the image. As shown in <FIG>, performing <NUM> edge detection occurs after the other image processing steps discussed above have been completed.

Performing <NUM> edge detection on the image <NUM> comprises performing Canny edge detection. Canny edge detection is a particular type of edge detection which uses a multistage algorithm to detect a wide range of edges in images. Firstly, a Gaussian filter is applied to the image to smooth the image and remove noise. The filter uses a Gaussian function of the form: <MAT> where x is the distance from the origin in the horizontal axis, y is the distance from the origin in the vertical axis and σ is the standard deviation of the Gaussian distribution.

Next, intensity gradients within the image are found. An edge detection operator (e.g. a Roberts, Prewitt or Sobel operator) is applied to the image to return the first derivative in the horizontal direction (Gx) and the vertical direction (Gy). From this, the edge gradient G and direction θ can be determined by: <MAT><MAT> The edge direction is rounded to one of four angles representing vertical, horizontal, and the two diagonals (i.e. <NUM>°, <NUM>°, <NUM>° and <NUM>°).

Once the intensity gradients have been found in the image <NUM>, a gradient magnitude threshold or lower bound cut-off suppression is applied to the image <NUM>. This is an edge thinning technique which is used to remove unwanted spurious points on the edges of the image <NUM>. Lower bound cut-off suppression is applied to find the locations with the sharpest change of intensity value. The edge strength of the current pixel is compared with the edge strength of the pixel in the positive and negative gradient directions. If the edge strength of the current pixel is the largest compared to the other pixels with the same direction (e.g. a pixel that is pointing in the y-direction will be compared to the pixel above and below it in the vertical axis), the value is preserved. Otherwise, the value will be suppressed.

A double threshold is then applied to determine potential edges and to suppress weak edges. Although the previous edge thinning techniques means that the remaining edge pixels provide a more accurate representation of the edges of the image, some spurious edge pixels may remain. To filter these out, high and low threshold values are selected. If an edge pixel's gradient value is smaller than the high threshold value and larger than the low threshold value, it is marked as a weak edge pixel. If an edge pixel's gradient value is smaller than the low threshold value, it is suppressed. The thresholds selected are dependent on the image being analysed and are empirically determined.

The detection of edges is finalised by suppressing any remaining weak edges which are not connected to strong edges. A weak edge which is associated with a true edge (i.e. an actual edge of an object in the image) is expected to be attached to a strong edge. Therefore, any weak edge which is not attached to a strong edge is likely caused by noise/colour variations and can be suppressed. This is done by looking at a weak edge pixel and its eight connected neighbourhood pixels. As long as there is one strong edge pixel within the connected neighbourhood pixels, the weak edge pixel can be identified as one that should be preserved.

Although Canny edge detection is used in the method <NUM> of <FIG>, in some examples, the edge detection may comprise other algorithms. For example, the edge detection may comprise Sobel, Prewitt or Roberts algorithms.

After edge detection has been applied to the image <NUM>, the method <NUM> comprises determining <NUM> a relative position of the tread <NUM> of the tyre <NUM>. Determining <NUM> the relative position comprises determining an angle of the tread <NUM> of the tyre <NUM> relative to the camera. As illustrated in <FIG>, the method comprises determining the angle of the tread by applying a Hough transform to the portion <NUM> to find straight lines (as indicated by dashed lines <NUM> in <FIG>) within the portion <NUM>. The straight lines <NUM> indicate the edges of the tread <NUM> and the angle of the lines <NUM> are indicative of the steering angle of the aircraft landing gear <NUM>.

The Hough transform is a feature extraction technique which is used to identify lines in an image. In polar coordinates, every point on a line can be described using a pair r, θ (also known as Hough space), where r is the shortest distance from the origin to the closest point on the line (approaching the line perpendicularly) and θ is the angle between the x-axis and a line connecting the origin with the closest point on the line. For a given line, specific r and θ values can be determined which satisfy the following equation for each point x, y: <MAT>.

Each point along a line in the portion <NUM> of the image <NUM> can be mapped as a sinusoid in Hough space, while each point in Hough space is mapped as a line in image space. Therefore, a point at which sinusoids intersect in Hough space indicates the presence of a straight line in the image <NUM>.

By applying a Hough transform to the edge detected image <NUM>, a relative angle of the tread <NUM> of the tyre <NUM> is determined. The relative angle of the tread <NUM> is the angle of the tread relative to the camera <NUM>. The method <NUM> then comprises determining <NUM> the steering angle of the aircraft landing gear, based at least in part the relative angle of the tread <NUM> of the tyre <NUM>. Where multiple lines <NUM> are determined from the Hough transform, a median of the angles of the lines <NUM> is taken to determine the steering angle of the aircraft landing gear <NUM>. In some example, another central tendency of the angles of the lines <NUM> may be determined, such as a mean or a mode of the angles of the lines <NUM>. To help to ensure that erroneous line lengths are disregarded (e.g. those too small to be an edge of the tread <NUM>), a predefined limit is applied to ignore such lines. The predefined limit is a minimum length (e.g. minimum number of pixels) for a line to be considered an edge of the tread <NUM>. Any lines below the predefined length are disregarded.

Although the method <NUM> has been described with relation to the component of the aircraft landing gear <NUM> being the tyre <NUM>, in some examples the component may be another part of the aircraft landing gear <NUM>. For example, the component may be the torque link <NUM>, where the torque link <NUM> comprises a reference mark <NUM> (as shown in <FIG>). In such an example, the determining <NUM> the relative position of the component comprises determining, based at least in part on the edge obtained by the edge detection, the relative position of the reference mark <NUM>. Although one reference mark <NUM> is shown in <FIG>, in some examples the component the torque link <NUM> comprises a plurality of such reference marks.

The reference mark <NUM> may help in determining the position of the torque link <NUM> relative the imaging device <NUM>. For example, the reference mark <NUM> may be less susceptible to external influence than the torque link <NUM> as a whole, e.g. it may be less likely that an external force moves the reference mark <NUM> in an undesirable/inconsistent way. Therefore, the reference mark <NUM> may provide a more consistent reference point for determining the position of the torque link <NUM>.

Furthermore, the reference mark <NUM> is arranged such that it is not occluded from the camera, even when a part of the torque link <NUM> is occluded from the camera. This may allow the position of the torque link <NUM> to be determined even when the part of the torque link is occluded. In other examples, the reference mark <NUM> is omitted and the steering angle is determined based on an edge of the torque link <NUM> obtained by the edge detection. In some examples, the component comprising the reference mark <NUM> is another component of the aircraft landing gear, for example a bogie of the aircraft landing gear <NUM>.

After the steering angle of the aircraft landing gear <NUM> has been determined, the method <NUM> comprises providing <NUM> information indicative of the steering angle of the aircraft landing gear <NUM> to the crew, e.g. the pilot, of the aircraft <NUM>. The steering angle, or the information indicative of the steering angle, is displayed in the cockpit <NUM> of the aircraft <NUM> such that it is easily available to the pilot. In this way, the pilot is informed of the steering angle of the aircraft landing gear even if the aircraft landing gear is not in their direct sight. This may help the pilot with manoeuvring the aircraft <NUM> on the ground.

The determined steering angle of the aircraft landing gear <NUM> is used to limit further movement/rotation of the aircraft landing gear <NUM>. For example, if the steering angle of the aircraft landing gear <NUM> is determined to be at a maximum safe steering angle, the pilot is prevented from increasing the steering angle further. The pilot may also receive a warning or other feedback when the maximum steering angle is reached. In some examples, when the pilot is using a physical input to control the steering angle, physical feedback through the input, e.g. shaking of the input, may be provided to the pilot to indicate when the maximum steering angle is reached.

In some examples, the information indicative of the steering angle of the aircraft landing gear <NUM> is provided to the crew of the aircraft <NUM> while the aircraft <NUM> is not on the ground. This information may be used to determine whether the aircraft landing gear <NUM> is in the correct position/orientation for landing and/or cruise. This may allow the crew to make an informed decision on whether it is safe to continue with landing and/or continued flight.

In some examples, the determined steering angle is stored, for example on the memory of the aircraft <NUM>. Additionally or alternatively, the determined steering angle is stored on a database accessible by the aircraft <NUM>, for example a database remote from the aircraft <NUM>. In this way, the determined steering angle can be used to aid future steering angle determination or can be reviewed post flight to determine and analyse the accuracy of the method <NUM>. As such, improvements can be made to the method <NUM> from a review of previously determined steering angles.

In some examples, any one or more of the steps of performing <NUM> image pre-processing, determining <NUM> the region of disinterest, applying <NUM> the mask, applying <NUM> the threshold, determining <NUM> the region of interest and/or providing <NUM> information may be omitted from the method <NUM>.

Although the method <NUM> utilises edge detection in determining the steering angle of the aircraft landing gear <NUM>, in some examples another form of feature detection may be used. For example, corner detection (where corners in an image are detected) or ridge detection (where ridges in an image are detected) may be used.

As the method <NUM> relies on the detection of edges of the component of the aircraft landing gear <NUM>, it may be more difficult to carry out this method <NUM> when the component is partially occluded from the camera <NUM>. An additional method <NUM> of determining the steering angle of the aircraft landing gear <NUM> which is not as affected by occlusion is shown in <FIG>.

<FIG> shows a flow chart of a further method <NUM> of determining a steering angle of an aircraft landing gear <NUM>. The method <NUM> is a computer-implemented method <NUM> of determining the steering angle of the aircraft landing gear <NUM>. The method <NUM> is carried out by the aircraft controller <NUM> and comprises obtaining <NUM> an input image of the aircraft landing gear <NUM> and comparing <NUM> the input image against a plurality of reference images, e.g. a set of reference images or database of reference images. The plurality of reference images comprises images of the aircraft landing gear <NUM> at known steering angles. The method <NUM> comprises determining <NUM> a most similar reference image, where the most similar reference image comprises the reference image of the plurality of reference images most closely matched to the input image, and determining <NUM>, based at least in part on the most similar reference image, the steering angle of the aircraft landing gear <NUM>.

<FIG> shows a schematic illustration of the input image <NUM> and a reference image <NUM> of the plurality of reference images <NUM>. In <FIG>, the input image <NUM> and the reference image <NUM> are shown split into their respective red 221a, 231a, green 221b, 231b and blue 221c, 231c component planes.

Before the input image <NUM> is compared against the plurality of reference images <NUM>, the method <NUM> comprises determining <NUM> a subset of the plurality of reference images <NUM> to be compared against the input image <NUM>. The subset is determined based on a previous determination of the steering angle of the aircraft landing gear <NUM>. As the steering angle will not be expected to have changed significantly from the previous determination, the subset can be focussed near to the previously determined steering angle to reduce the number of comparisons that are needed to be made. For example, when the steering angle has previously been determined to be at +<NUM>° (i.e. <NUM>° clockwise, as viewed from above, such that the aircraft <NUM> would turn to the right when moving forward), the subset comprises reference images <NUM> which have steering angles within a predefined range of this steering angle. For example, when the predefined range is <NUM>°, the subset will include the reference images <NUM> with a steering angle of between +<NUM>° and +<NUM>°. Alternatively, the predefined range may be <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>° or any other suitable range. Although the subset is determined after the input image <NUM> has been obtained in <FIG>, in some examples, the subset is determined before the input image <NUM> is obtained.

As the input image <NUM> obtained by the method <NUM> may not be in optimal condition to be compared against the plurality of reference images <NUM>, the input image <NUM> is modified to help to address this. For example, the input image <NUM> may contain excess noise which should be removed/reduced to improve the subsequent comparison. Therefore, after the input image is obtained, the method <NUM> comprises applying <NUM> a filter to the input image <NUM> to greyscale the image and processing <NUM> the input image to remove background noise from the input image. To grayscale the input image <NUM>, each pixel of the input image <NUM> is converted to a shade of grey based on the brightness/luminance of the pixel. To remove noise from the input image <NUM>, a smoothing filter, such as a Gaussian smoothing, is applied to the input image <NUM>.

When the input image <NUM> is captured, the brightness (or perceived brightness) of the input image <NUM> may be inconsistent with the brightness of each of the plurality of reference images <NUM>. For example, where the input image <NUM> is captured at night, and the plurality of reference images were captured in daylight, there will be a difference in light levels between the images. Where the input image <NUM> is too dark, this may obscure features of the input image <NUM> which are to be compared with the reference images <NUM>. To help to address this, the method <NUM> comprises performing <NUM> gamma correction on the input image <NUM>. The gamma correction is based on the gamma level of the plurality of reference images <NUM> and may help to improve the consistency of gamma levels between the input image <NUM> and the plurality of reference images <NUM>. This may help to increase the accuracy of the comparison of the input image <NUM> against the plurality of reference images <NUM>. The gamma correction may take the form as discussed in relation to the method of <FIG> above.

In some examples, both the input image <NUM> and the plurality of reference images <NUM> are processed in the same way. The processing may comprise at least one of masking, histogram matching, cropping, blurring, contrast limited adaptive histogram matching, sharpening and denoising.

To mask the input image <NUM> and the plurality of reference images <NUM>, reference images in the plurality of reference images <NUM> are compared against each other to determine how much corresponding pixels (and their pixel values) change between different reference images <NUM>. Where a pixel value changes by less than a predetermined threshold value, those pixels are determined to be part of the background of the reference image <NUM> and are therefore not relevant to the determination of steering angle of the aircraft landing gear <NUM>. The pixels which are determined to be part of the background are then masked from the input image <NUM> and the plurality of reference images <NUM>.

Histogram matching comprises modifying the input image <NUM> such that a histogram of the input image <NUM> matches a histogram of the plurality of reference images <NUM>. In some examples, the input image <NUM> and the plurality of reference images <NUM> are modified such that the histograms of the input image <NUM> and the plurality of reference images <NUM> match a predetermined histogram. Alternatively, contrast limited adaptive histogram matching may be used to match the histogram of the input image <NUM> and the histogram of the plurality of reference images <NUM>.

As only a portion of the input image <NUM> and the plurality of reference images <NUM> (such as a portion containing the aircraft landing gear <NUM>) may be of interest when determining the steering angle, in some examples, the input image <NUM> and the plurality of reference images <NUM> may be cropped to only include a predetermined steering region. The steering region is a region within the input image <NUM> and the plurality of reference images <NUM> which includes the parts of the aircraft landing gear <NUM> that move when the steering angle changes.

In some examples, the input image <NUM> and the plurality of reference images <NUM> are smoothed by blurring. Blurring of the input image <NUM> and the plurality of reference images <NUM> is achieved by convolving the images with a low-pass filter kernel. The blurring may comprise averaging, Gaussian blurring, median blurring and/or bilateral filtering.

To sharpen the input image <NUM> and the plurality of reference images <NUM>, in some examples, a sharpening kernel, such as that shown below, is applied to the input image <NUM> and each reference image <NUM> of the plurality of reference images <NUM>.

In some examples, denoising the input image <NUM> and/or the plurality of reference images <NUM> comprises applying a non-local means denoising algorithm to the input image <NUM> and/or the plurality of reference images <NUM>. Non-local means denoising comprises replacing the colour of an individual pixel in an image with an average of colours of similar pixels within the image.

After the input image <NUM> has been modified as desired, the method <NUM> comprises comparing <NUM> the input image <NUM> against the subset of the plurality of reference images <NUM> and determining <NUM> a most similar reference image <NUM>. The most similar reference image <NUM> is the reference image <NUM> of the plurality of reference images <NUM> most closely matched to the input image <NUM>. Comparing <NUM> the input image <NUM> against the plurality of reference images <NUM> comprises comparing input image pixel values of the input image <NUM> against corresponding reference image pixel values of each of the plurality of reference images <NUM>. If a suitable match (i.e. a reference image <NUM> having a similarity characteristic within a predetermined threshold of the input image <NUM>) is not found in the subset, the subset is expanded to include additional reference images <NUM> until a suitable match is found.

In <FIG>, the input image pixel values and the reference image pixel values comprise information indicative of the colours of pixels in the input image <NUM> and reference images <NUM> respectively. The input image <NUM> and the reference image <NUM> each comprise a red component value 221a, 231a, a green component value 221b, 231b and a blue component value 221c, 231c. When comparing the input image <NUM> against the reference image <NUM>, the values and/or relative values of the red, green and/or blue component values of the individual pixels are compared. To do this, the method <NUM> comprises calculating a similarity metric between the input image <NUM> and the reference image <NUM>. In this case, the method comprises calculating the root mean square deviation (RMSD) between the input image pixel values and the corresponding reference image pixel values. This calculation is carried out for each of the reference images <NUM> in the subset, and the reference image <NUM> with the smallest RMSD between the input image pixel values and the reference image pixel values is determined to be the most similar reference image <NUM>. In some examples, a different similarity metric is calculated. For example, the similarity metric may comprise the total number of pixel values in common between the input image <NUM> and the reference image <NUM>. A respective similarity metric for each reference image may be determined and the reference image <NUM> with the similarity metric closest to a threshold value may be determined, wherein the threshold value is indicative of the highest degree of similarity. Another similarity metric which may be used is a cosine similarity. In some examples, the input image pixel values are transformed into a one-dimensional input image vector and the reference image pixel values are transformed into a one-dimensional reference image vector. The cosine similarity between the input image vector and the reference image vector is then calculated. This calculation is carried out for each of the reference images <NUM> in the subset, and the reference image <NUM> with the greatest cosine similarity between the input image vector and the reference image vector is determined to be the most similar reference image <NUM>.

Although the components of the colours of the pixels are compared in the method of <FIG>, in some examples, such as when the input image <NUM> and the reference images <NUM> are not in colour, the input image pixels values and the reference image pixel values may comprise the intensity of the relevant pixels. The most similar reference image <NUM> is then the reference image <NUM> with the most pixels having the same or similar intensity as in the input image <NUM>.

It is also possible to use machine learning to determine the most similar reference image <NUM>. For example, determining <NUM> the most similar reference image <NUM> may comprise utilising a machine learning algorithm. The machine learning algorithm may comprise the input image <NUM> as an input, and the most similar reference image <NUM> as an output. Such a machine learning algorithm may be trained to provide its output based on a set of training data, for example a set of training data labelled with ground truth values in a supervised learning process. For example, the plurality of reference images <NUM> at known steering angles may be used to train the machine learning algorithm. In another example, measured data may form a training data set. In some examples, the machine learning algorithm may be updated in real-time based on data obtained by the aircraft <NUM>, or other aircraft of the same type. In some examples, the machine learning algorithm may comprise a neural network.

Based at least in part on the most similar reference image <NUM>, the method <NUM> comprises determining <NUM> the steering angle of the aircraft landing gear <NUM>. As the steering angle of each of the reference images <NUM> is known, the steering angle of the most similar reference image <NUM> is determined to be the steering angle of the aircraft landing gear <NUM>.

Once the steering angle has been determined, the method <NUM> comprises providing <NUM> information indicative of the steering angle of the aircraft landing gear <NUM> to the crew, e.g. the pilot, of the aircraft <NUM>. The steering angle, or the information indicative of the steering angle, is displayed in the cockpit <NUM> of the aircraft <NUM> such that it is easily available to the pilot. In this way, the pilot is informed of the steering angle of the aircraft landing gear even if the aircraft landing gear is not in their direct sight. This may help the pilot with manoeuvring the aircraft <NUM> on the ground.

In some examples, the information indicative of the steering angle of the aircraft landing gear <NUM> is provided to the crew of the aircraft <NUM> while the aircraft <NUM> is not on the ground. This information may be used to determine whether the aircraft landing gear is in the correct position/orientation for landing and/or cruise. This may allow the crew to make an informed decision on whether it is safe to continue with landing and/or continued flight.

In some examples, the determined steering angle is stored, for example on the memory of the aircraft <NUM>. Additionally or alternatively, the determined steering angle is stored on a database accessible by the aircraft, for example a database remote from the aircraft <NUM>. In this way, the determined steering angle can be used to aid future steering angle determination or can be reviewed post flight to determine and analyse the accuracy of the method <NUM>. As such, improvements can be made to the method <NUM> from a review of previously determined steering angles.

Although the method <NUM> as described above compares the input image <NUM> against the subset of the plurality of reference images <NUM>, in some examples the method <NUM> compares the input image <NUM> against all of the plurality of reference images <NUM>. The plurality of reference images <NUM> comprises a series of images of the aircraft landing gear <NUM> where there is an interval of between <NUM>° and <NUM>° of steering angle between each image. In some examples, the interval may be between <NUM>° and <NUM>°, <NUM>° and <NUM>°, <NUM>° and <NUM>°, <NUM>° and <NUM>°, or <NUM>° and <NUM>°. The plurality of reference images covers a total range of steering angles of the aircraft landing gear <NUM> of <NUM>°, i.e. between -<NUM>° (<NUM>° to the left) and +<NUM>° (<NUM>° to the right). In some examples, the total range is between <NUM>° and <NUM>°, between <NUM>° and <NUM>° or between <NUM>° and <NUM>°. In some examples, other ranges are also possible.

The plurality of reference images <NUM> are generated by capturing images of the aircraft landing gear <NUM> while at the same time receiving information indicative of the steering angle of the aircraft landing gear <NUM>. The images and their corresponding steering angles are saved to the memory, along with a mapping between the reference image <NUM> and the corresponding steering angle. In some examples, the information indicative of the steering angle is determined by a sensor, e.g. a rotary variable differential transformer (RVDT), on the aircraft landing gear <NUM>.

The plurality of reference images <NUM> are stored on the memory of the aircraft <NUM>. In this way, the plurality of reference images <NUM> can be quickly accessed by the method <NUM>. In some examples, the reference images are stored on a database accessible by the aircraft <NUM>, such as a database remote from the aircraft <NUM>. This may allow multiple aircraft <NUM> to access the plurality of reference images <NUM> and may also allow the plurality of reference images <NUM> to be updated at a central location, rather than having to update the memory of each aircraft <NUM> individually.

An implementation of the method <NUM> is further illustrated in the flow chart <NUM> of <FIG>. A reference dataset (e.g. a plurality of reference images) is generated (box <NUM>) by capturing images (box <NUM>) of the aircraft landing gear with the camera while measuring <NUM> (box <NUM>) the steering angle of the aircraft landing gear <NUM> and mapping the images to the corresponding measured steering angle, such that the steering angle of each frame captured by the camera is known (box <NUM>). An input image <NUM> (box <NUM>) from the camera is compared against the reference dataset to compute similarities (box <NUM>). From this, the RMSD of each image in the reference dataset compared to the input image <NUM> is calculated (box <NUM>). The reference image with the lowest associated RMSD is found (box <NUM>) and is used to predict (box <NUM>) the steering angle of the input image <NUM> and therefore the aircraft landing gear <NUM>.

In some examples, any one or more of the steps of determining <NUM> the subset, applying <NUM> the filter, processing <NUM> the input image, performing <NUM> the gamma correction and/or providing <NUM> information may be omitted from the method <NUM>.

As discussed above, the method <NUM> of <FIG> may work better in certain situations than the method <NUM> of <FIG>. As such, it is advantageous to be able to select between the two methods <NUM>, <NUM> when attempting to determine the steering angle of an aircraft landing gear <NUM>. This may allow the best method <NUM>, <NUM> to be used for the given conditions. <FIG> shows a flow chart of such a method <NUM> of determining a steering angle of the aircraft landing gear <NUM>. The method <NUM> comprises determining <NUM> a condition affecting the aircraft landing gear <NUM>. The condition is a determination of whether the tyre <NUM> of the aircraft landing gear is occluded from the camera. In other examples, the condition may be another condition affecting the determination of the steering angle of the aircraft landing gear <NUM>, such as a weather condition and/or an operation status of the imaging device <NUM>.

The method <NUM> comprises selecting <NUM> a first mode of determining the steering angle of the aircraft landing gear <NUM> or a second mode determining the steering angle of the aircraft landing gear <NUM>, based at least in part on the condition. The first mode is the method <NUM> as described in relation to <FIG> and the second mode is the method <NUM> as described in relation to <FIG>. As such, the optimal or preferred method for determining the steering angle of the aircraft landing gear <NUM> given the condition can be selected. Once the mode has been selected, the method <NUM> comprises determining <NUM> the steering angle of the aircraft landing gear <NUM> using the selected mode.

As the first and second modes (and the methods <NUM>, <NUM>) are different method of determining the steering angle of the aircraft landing gear <NUM>, the first and second modes may have different failure conditions. As such, when one of the modes/methods is not working as desired, the other mode/method can be selected. By being able to expressly select the first or second mode, the optimal mode can be selected without having to rely on any other input/information. For example, it is not necessary for one of the modes to fail before the other mode is used.

Although in the above example the first mode is the method <NUM> as described in relation to <FIG> and the second mode is the method <NUM> as described in relation to <FIG>, in other examples the first mode and/or the second mode may comprise another method of determining the steering angle of the aircraft landing gear <NUM>. The first mode and/or the second mode may comprise using a sensor, such as a rotary variable differential transformer (RVDT), to determine the steering angle of the aircraft landing gear <NUM>, or may comprise using another suitable form of computer vision to determine the steering angle of the aircraft landing gear <NUM>.

<FIG> shows a schematic diagram of a non-transitory computer-readable storage medium <NUM> according to an example. The non-transitory computer-readable storage medium <NUM> stores instructions <NUM> that, if executed by a processor <NUM> of an aircraft controller <NUM>, cause the processor <NUM> to perform one of the methods described herein. In some examples, the aircraft controller <NUM> is the aircraft controller <NUM> described above with reference to <FIG> or a variant thereof described herein. The instructions <NUM> may comprise instructions to perform any of the methods <NUM>, <NUM>, <NUM> described above with reference to <FIG> or variants thereof, such as those discussed herein.

Any step or feature discussed in relation to one of the methods <NUM>, <NUM>, <NUM> discussed herein may be used in combination with the steps and features of any other method <NUM>, <NUM>, <NUM> discussed herein.

It is to noted that the term "or" as used herein is to be interpreted to mean "and/or", unless expressly stated otherwise.

Claim 1:
A computer-implemented method of determining a steering angle of an aircraft landing gear (<NUM>), wherein the steering angle is adjusted for turning an aircraft (<NUM>) during movement on the ground, the method comprising:
obtaining an input image (<NUM>, <NUM>) of the aircraft landing gear, wherein the input image is captured by an imaging device (<NUM>) positioned on a strut of the aircraft landing gear so as to be directly above a tyre of the aircraft landing gear when the landing gear is extended;
comparing the input image against a plurality of reference images (<NUM>), the plurality of reference images comprising images of the aircraft landing gear at known steering angles;
determining a most similar reference image, the most similar reference image comprising a reference image of the plurality of reference images most closely matched to the input image; and
determining, based at least in part on the most similar reference image, the steering angle of the aircraft landing gear.