Patent Description:
In-person human-based inspections of structures and various types of objects can be time consuming, expensive and difficult for an individual to perform. Examples of structures that pose significant inspection challenges include bridges, dams, levees, power plants, power lines or electrical power grids, water treatment facilities, oil refineries, chemical processing plants, high-rise buildings, infrastructure associated with electric trains and monorail support structures, and aircraft structures at airports.

Utilizing an unmanned aerial vehicle (UAV), an operator can safely acquire images or other sensor data from structures. The UAV operator can initiate an automatic scanning process of structures without being placed in harm's way and without requiring cumbersome and expensive equipment, such as cranes or platforms. A typical unmanned aerial vehicle, however, does not have the ability to acquire accurate data representing the distances between points on an object that might be encountered during flight or the relative scale of objects seen by the on-board camera. For example, while GPS-equipped UAVs can provide a rough estimate of location sufficient for visual inspection, GPS tracking is not accurate enough for use in other non-destructive inspection methods.

The primary method of situational awareness currently available for users of remotely operated mobile platforms such as UAVs is watching a display monitor showing the video from the on-board camera. One of the usability concerns with that approach is that the operator does not have a frame of reference to determine the size of the objects displayed on the screen, which limits the usefulness of inspection applications with these platforms. Another approach to getting scale information is to use an image processing application to identify objects in the scene, but that only works if you have information about objects in the scene and the software is capable of properly identifying them. A further approach is to use depth cameras to measure the distance to the target object, but depth cameras can saturate in bright light and have limited range. Yet another approach is to use a spinning laser scanner on-board the mobile platform to provide a point cloud with distance data, but this methodology acquires more data and is more complex than is needed for measuring the distance to the target object.

The document <CIT> states, in accordance with its abstract, devices and methods of autonomously inspecting elongated structures, such as blades of wind turbines. An unmanned aerial vehicle (UAV) is guided towards the elongated structure. The UAV automatically senses distance from the elongated structure. The UAV autonomously maintains a distance greater than a safety distance and identifies an optimum inspecting distance from the elongated structure. The UAV is then autonomously placed at the optimum inspecting distance to automatically record data pertinent to at least a region of the elongated structure when the UAV is at the optimum inspecting distance.

Accordingly, it would be desirable to provide the ability to determine distances between points on or sizes of objects appearing in captured images during UAV-based non-destructive inspection.

The subject matter disclosed is directed to a method, as defined in claim <NUM>, for acquiring scale for a structure undergoing non-destructive inspection.

The concepts described herein provide measurement and control capabilities for UAVs and other remotely operated mobile platforms. The acquisition of measurement data will be divided into the following three categories.

Two or more laser pointers and a digital video camera are used to acquire the information to compute: distance to the target, a reference scale for the view of the target, and in some examples, distance between points of interest on the target. This category of examples is applicable to situations where the target surface is relatively flat and perpendicular to the aim direction of the laser pointers and camera. As used herein, the term "laser pointer" means a device that emits a laser beam and does not detect returned laser light.

Another category of non-claimed examples are configurations where UAV contains two or more laser range meters that enables: direct measurement of distance to the target, reference scale, as well as one or more orientation angle of the UAV relative to the target. If three non-collinearly mounted laser range meters are used (not shown here), more than one orientation angle can be measured (for example yaw and pitch). As used herein, the term "laser range meter" (also known as "laser rangefinder") means a device that emits a laser beam and detects returned laser light.

A third category of non-claimed examples includes a gimbaled laser range meter is used to acquire distance and aim direction information from the moving platform (e.g., UAV) to objects in the environment. This concept leverages some aspects of the vector-based measurement algorithms disclosed in <CIT>, along with the addition of sensors, such as an inertial measurement unit, to determine the relative motion of the platform. This platform motion data along with the aim direction and distance data from the gimbaled laser range meter can be used to acquire measurements of objects in the environment.

Although various examples of systems and methods for acquiring scale and point-to-point distance information for objects undergoing non-destructive inspection are described in some detail later herein.

Other aspects of systems and methods for acquiring scale and point-to-point distance information for objects in an environment using a remotely operated flying platform are disclosed below.

The features, functions and advantages discussed in the preceding section can be achieved independently in various examples or may be combined in yet other examples. Various examples will be hereinafter described with reference to drawings for the purpose of illustrating the above-described and other aspects. None of the diagrams briefly described in this section are drawn to scale.

Reference will hereinafter be made to the drawings in which similar elements in different drawings bear the same reference numerals.

For the purpose of illustration, systems and methods for acquiring scale and point-to-point distance information for objects undergoing aerial non-destructive inspection using a UAV will now be described in detail. However, not all features of an actual implementation are described in this specification. A person skilled in the art will appreciate that in the development of any such example, numerous implementation-specific decisions must be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

<FIG> is a diagram showing a system for inspecting a bridge <NUM>. The system includes an unmanned aerial vehicle <NUM> (hereinafter "UAV <NUM>") that may be moved (flown) around a structure requiring periodic inspection. In this example the UAV <NUM> is a rotorcraft. While the structure being inspected is illustrated as a bridge <NUM>, the system is equally well adapted for use in inspecting a wide range of other structures including, but not limited to, power lines, power-generating facilities, power grids, dams, levees, stadiums, large buildings, large antennas and telescopes, water treatment facilities, oil refineries, chemical processing plants, high-rise buildings, and infrastructure associated with electric trains and monorail support structures. The system is also particularly well suited for use inside large buildings such as manufacturing facilities and warehouses. Virtually any structure that would be difficult, costly, or too hazardous to inspect by a human controlling the inspection device or the platform carrying the inspection device may potentially be inspected using the system depicted in <FIG>.

For inspection applications, a rotorcraft is preferred due to its ability to hover and move at very slow speeds. The vertical take-off and landing capability of remote-controlled unmanned rotorcraft also may be highly advantageous in many applications, especially when operating inside of structures or facilities such as manufacturing plants, warehouses, etc., or when inspecting complex facilities such as oil refineries or chemical processing that may have many tall structures (e.g., smoke stacks) clustered closely together. The ability to hover and/or move only vertically enables remote-controlled unmanned rotorcraft to fly close to and inspect large vertical structures such as vertical support posts of bridges, antennas or vertical surfaces of dams.

In accordance with some examples (disclosed in more detail below), the UAV <NUM> comprises a frame <NUM> that supports a pair of laser devices 24a and 24b arranged on opposite sides of a camera <NUM>. The camera <NUM> may comprise a still camera (color and/or black and white) to obtain still images, a video camera to obtain color and/or black and white video, or an infrared camera to obtain infrared still images or infrared video of portions of bridge <NUM>. The laser devices 24a and 24b emit respective laser beams 26a and 26b which are directed toward a portion of the bridge <NUM>. As will be explained in some detail below, the impingement of laser beams 26a and 26b on a surface of the bridge enables the acquisition of information concerning the location of the UAV <NUM> relative to the bridge <NUM>.

The system depicted in <FIG> further comprises a remote control station <NUM> for sending and receiving wireless communications to and from the UAV <NUM>. In accordance with one example, the remote control station <NUM> comprises a laptop computer <NUM>, a transceiver <NUM> and an antenna <NUM>. The transceiver <NUM> is in communication with the antenna <NUM> for enabling communication between the laptop computer <NUM> and the UAV <NUM>.

The on-board system of the UAV <NUM> may further comprise a guidance and control hardware and software system (not shown in <FIG>) that is able to implement one or more different, stored flight plans digitally represented by flight plan data stored in a non-transitory tangible computer-readable storage medium (not shown in <FIG>). The on-board system may further comprise a global positioning system/inertial navigation system (GPS/INS) for controlling the orientation of UAV <NUM> and assisting in carrying out the preprogrammed flight plan stored in memory. A wireless transceiver and an on-board antenna (not shown in <FIG>) enable bidirectional, wireless electromagnetic wave communications with the remote control station <NUM>.

Unmanned aerial vehicles of the type depicted in <FIG> may be upgraded with the capability to acquire scale and point-to-point distance information for objects undergoing non-destructive inspection. The UAV may be provided with on-board sensors and processing techniques to provide discrete or continuous measurements of the distances between points on a target object or the scale of the target object. Various examples of such an enhanced-capability UAV will now be described in some detail.

<FIG> is a diagram showing a top view of one example of an airborne UAV <NUM> that is capable of acquiring scale and point-to-point distance information for objects undergoing non-destructive inspection. The UAV <NUM> comprises a pair of laser pointers 132a and 132b arranged in a parallel configuration. The laser pointers 132a and 132b emit respective laser beams along respective optical paths indicated by respective aim direction vectors 134a and 134b. The UAV <NUM> further comprises a digital video camera (not shown in <FIG>). The laser pointers 132a and 132b and the video camera are used to calculate distance to target object <NUM> and reference scale. This example is used for situations where the UAV <NUM> is relatively close to the target object <NUM>.

The UAV <NUM> depicted in <FIG> comprises a frame <NUM> and four rotors 124a-124d rotatably mounted to the frame <NUM>. Rotation of each rotor is driven by a respective motor (not shown in <FIG>) mounted to the frame <NUM>. The pair of laser pointers 132a and 132b having mutually parallel axes are fixedly mounted to the frame <NUM>. When activated, the laser pointers 132a and 132b direct respective mutually parallel laser beams at respective laser spots on a surface of a target object <NUM>. Although not shown in <FIG>, the UAV <NUM> also comprises a video camera <NUM> (see <FIG>) mounted to the frame <NUM>. Preferably the focal axis of the video camera <NUM> and the aim directions of the laser pointers 132a and 132b are mutually parallel.

The video camera <NUM> may be activated to capture an image in which the two laser spots are visible. This image data can be processed (as described in some detail below) to derive pixel information which, in conjunction with the known distance separating the axes of the two laser pointers 132a and 132b, can be used to determine a scale factor. That scale factor is used to display a scale indicator on an image captured by the video camera <NUM> while the UAV is hovering at the same location. More specifically, one goal is to determine the distance D between the pointers 132a and 132b and the target object <NUM>, as will be described in more detail below with reference to <FIG>.

<FIG> is a block diagram identifying some components of a system for performing non-destructive inspection of a structure using a remote-controlled UAV <NUM> having two or more laser pointers <NUM> (e.g., a first laser pointer 132a and a second laser pointer 132b as seen in <FIG>) mounted thereon. In this example, the UAV <NUM> and the equipment carried by the UAV <NUM> are controlled by the on-board computer system <NUM> as a function of radiofrequency commands transmitted by a control station <NUM>. Those radiofrequency commands are received by a transceiver <NUM> on-board the UAV <NUM>, converted into the proper digital format and then forwarded to the computer system <NUM>. The control station <NUM> may comprise a general-purpose computer system configured with programming for controlling operation of the UAV <NUM> and the equipment on-board the UAV <NUM> by sending commands to the computer system <NUM>. For example, the control station may send commands controlling the flight of the UAV <NUM> and commands for activation of the laser pointers <NUM>. In addition, the computer system at the control station <NUM> is configured with programming for processing data received from the UAV <NUM> during an inspection operation. In particular, the computer system of the control station <NUM> may comprise a display processor configured with software for controlling a display monitor <NUM> to display images acquired by the video camera <NUM>. The optical image field, as sighted by the video camera <NUM>, can be displayed on the display monitor <NUM>.

In response to commands from the control station <NUM>, the video camera <NUM> and the laser pointers <NUM> can be activated by control signals (e.g., via electrical cables) transmitted by the computer system <NUM>. The video camera <NUM> may have automated (remotely controlled) zoom capabilities. The computer system <NUM> also controls the flight of the UAV <NUM> by sending commands to the motor controllers <NUM> which respectively control the rotation of respective motors <NUM> that drive rotation of rotors 124a-124d (see <FIG>).

<FIG> is a diagram showing a video camera <NUM> and a pair of laser pointers 132a and 132b separated from a target object <NUM> by the distance D, which laser pointers produce respective laser spots on the surface of the target object <NUM>. These laser spots on the target object surface are separated by the distance d. <FIG> is a diagram representing an image <NUM> acquired by the video camera <NUM> depicted in <FIG>, which image <NUM> includes a representation <NUM>' of the target object <NUM> and respective representations of the respective positions <NUM> and <NUM> of the laser spots.

In accordance with the situation depicted in <FIG>, the known variables are the current field-of-view of the video camera <NUM> (i.e., "FoV' in <FIG>), the maximum number of pixels in the width direction of the image <NUM> (i.e., "maxPx" in <FIG>), the number of pixels in the image <NUM> between the respective groups of pixels representing positions <NUM> and <NUM> of the laser spots produced by laser pointers 132a and 132b (i.e., "nPx," in <FIG>); and the distance separating the laser pointers 132a and 132b (i.e., "L<NUM>" in <FIG>). The unknown variables are the viewing angle α between the laser spots and the distances D and d.

The viewing angle α between the laser spots can be computed using the camera field-of-view (FoV) and image pixel data: <MAT> where nPx is the measured number of pixels between laser spots, and maxPx is the image width in pixels. Then the distances d and D can be computed using the following equations: <MAT> <MAT> Substituting Eq. (<NUM>) for the viewing angle α, one obtains: <MAT> In accordance with the example depicted in <FIG> and <FIG> (and other examples described hereinafter), the value of the distance D is updated continuously.

The value of the distance d may be included anywhere in the image <NUM> displayed on the display monitor (item <NUM> in <FIG>). A scale factor can be calculated based on a ratio of the distance d and the number of pixels nPx, and the scale indicator indicating the scale factor can be included as part of the image <NUM>. This scale indicator will be accurate so long as the UAV-target object separation distance D is up-to-date. As that separation distance changes, the operations described above can be repeated to generate an updated scale factor. Over time, the scale indicator is repeatedly adjusted as a function of the variable distance separating the UAV and the target object.

For the purpose of non-destructive inspection, preferably the acquired images of the inspected structure do not include representations of laser spots. Accordingly, following the initial sizing of the imaged surface area of the target object, the video camera <NUM> can be activated to capture additional images (e.g., a video sequence of images) while the laser pointers 132a and 132b are de-activated. In this case, the video camera <NUM> preferably captures images while the separation distance D is up-to-date.

For example, <FIG> is a diagram representing an image <NUM> that includes a representation of a portion of a structure <NUM> having a visible anomaly <NUM> and a scale bar <NUM>, but does not include any representations of laser spots. A technician at the control station <NUM> can view this image while appreciating the applicable size of the imaged area as indicated by the scale bar <NUM>. In addition, visible anomalies can be roughly (i.e., approximately) sized by comparing a visible dimension of the anomaly to a visible dimension of the scale indicator appearing on the display screen.

<FIG> is a diagram showing a top view of an airborne UAV <NUM> in accordance with an alternative example. Like the example partly depicted in <FIG>, the example partly depicted in <FIG> is also capable of acquiring scale and point-to-point distance information for objects undergoing non-destructive inspection. The UAV <NUM> partly depicted in <FIG> comprises a pair of pivotable laser pointers 132a and 132b and a video camera <NUM> (not shown in <FIG>). When activated, the laser pointers 132a and 132b direct respective laser beams at respective laser spots on a surface of a target object <NUM>. The laser pointers 132a and 132b may be independently pivotable or their pivoting mechanism may be coupled so that the laser pointers 132a and 132b are oppositely pivotable. As used herein, the phrase "oppositely pivotable" means that the angular positions of the laser pointers 132a and 132b relative to the focal axis (not shown in <FIG>) of the video camera <NUM> pivot are equal and opposite at all times.

The laser pointers 132a and 132b can be rotated on-board the UAV <NUM> by a known amount relative to the parallel configuration. This creates additional separation between the laser spots on the target object <NUM>, which is useful for situations where the UAV <NUM> is further from the target object <NUM> than may be the case for the example depicted in <FIG>. For example, initially the laser pointers 132a and 132b are positioned in parallel to emit respective laser beams along mutually parallel optical paths indicated by respective aim direction vectors 134a and 134b. Then the laser pointers 132a and 132b are rotated by a known angle and then activated again to emit respective laser beams along optical paths indicated by respective aim direction vectors 134a' and 134b'. The distance to the target object <NUM> can be determined using images captured by the on-board video camera (not shown), which images include the groups of pixels representing the laser spots. More specifically, this example is configured to determine the distance d between the laser spots respectively produced on the target object <NUM> by the laser pointers 132a and 132b; and the distance D between the pointers 132a and 132b and the target object <NUM>, as will be described in more detail below with reference to <FIG>.

<FIG> is a diagram showing a video camera <NUM> and a pair of pivotable laser pointers 132a and 132b separated from a target object <NUM> by the distance D, which laser pointers produce respective laser spots on the surface of the target object <NUM>. These laser spots on the target object surface are separated by the distance d. <FIG> is a diagram representing an image <NUM> acquired by the video camera <NUM> depicted in <FIG>, which image <NUM> includes a representation <NUM>' of the target object <NUM> and respective groups of pixels representing respective positions <NUM> and <NUM> of the laser spots.

In accordance with the situation depicted in <FIG>, the known variables are the field-of-view of the video camera <NUM> (i.e., "FoV" in <FIG>), the maximum number of pixels in the width direction of the image <NUM> (i.e., "maxPx" in <FIG>), the number of pixels between the respective groups of pixels representing positions <NUM> and <NUM> of the laser spots produced by laser pointers 132a and 132b (i.e., "nPx," in <FIG>); the angle between the laser beams transmitted by the laser pointers 132a and 132b (i.e., "β" in <FIG>); and the distance separating the respective pivot axes of the laser pointers 132a and 132b (i.e., "L<NUM>" in <FIG>). The unknown variables are the viewing angle α between the laser spots and the distances D and d.

The viewing angle α between the laser spots can again be computed using Eq. (<NUM>). Then the distances d and D can be computed using the following equations: <MAT> <MAT>.

A scale factor can be calculated based on a ratio of the distance d and the number of pixels nPx, and the scale indicator indicating the scale factor can be displayed on an image captured by the video camera <NUM> while the UAV <NUM> hovers at the same location.

<FIG> is a diagram showing a top view of an airborne UAV <NUM> having a pair of fixed laser pointers 132a and 132b and a pivotable (about a single axis) third laser pointer 132c directed at a target object <NUM>. A camera on-board the UAV is not shown. In accordance with this variation, the two laser pointers 132a and 132b are mutually parallel and the third laser pointer 132c is rotated at a fixed or controllable angle relative to the other two. The third laser pointer 132c may emit laser light having a different color than the laser light emitted by laser pointers 132a and 132b to help differentiate the laser spots from each other on the target object <NUM>. (In the alternative, this computational method can be made to use three laser pointers of the same color. ) The laser pointers 134a and 134b emit respective laser beams along mutually parallel optical paths indicated by respective aim direction vectors 134a and 134b, while the third laser pointer 132c emits a laser beam along the optical path indicated by aim direction vector 134a in <FIG>.

<FIG> is a diagram showing a video camera <NUM> and three laser pointers 132a-c configured as depicted in <FIG> and separated from a target object <NUM> by the distance D, which laser pointers 132a-c produce respective laser spots, the furthest apart of which are separated by a distance d on the surface of the target object <NUM>. The laser spots produced on the target object surface by mutually laser pointers 132a and 132b are separated by the distance L<NUM>, which is also the physical distance separating the axes of laser pointers 132a and 132b. The laser spots produced on the target object surface by laser pointers 132a and 132b are separated by the distance d. <FIG> is a diagram representing an image <NUM> acquired by the video camera <NUM> depicted in <FIG>, which image <NUM> includes a representation <NUM>' of the target object <NUM> and respective groups of pixels representing respective positions <NUM>, <NUM> and <NUM> of the laser spots.

In accordance with the situation depicted in <FIG>, the known variables are the field-of-view of the video camera <NUM> (i.e., "FoV' in <FIG>), the maximum number of pixels in the width direction of the image <NUM> (i.e., "maxPx" in <FIG>), the number of pixels between the respective groups of pixels representing respective positions <NUM> and <NUM> of the laser spots produced by laser pointers 132a and 132b (i.e., "nPx<NUM>" in <FIG>); the number of pixels between the respective groups of pixels representing respective positions <NUM> and <NUM> of the laser spots produced by laser pointers 132b and 132c (i.e., "nPx<NUM>," in <FIG>); the angle between the laser beams transmitted by the laser pointers 132b and 132c (i.e., "β" in <FIG>); and the distance separating the respective axes of the laser pointers 132a and 132b (i.e., "L<NUM>" in <FIG>). The unknown variables are the viewing angle α between the laser spots and the distances D and d.

The viewing angle α between the laser spots produced by laser pointers 132a and 132b can again be computed using Eq. (<NUM>). Then the distances d and D can be computed using the following equations: <MAT> <MAT> or <MAT> Thus there are two ways to calculate d: one uses the angle β and the other uses nPx<NUM>. Having two separate ways of calculating the value for distance d serves as a process check to improve reliability.

A scale factor can be calculated based on a ratio of the distance d and the sum (nPx<NUM> + nPx<NUM>). Thereafter the scale indicator indicating the scale factor can be displayed on an image captured by the video camera <NUM> while the UAV <NUM> hovers at the same location.

In accordance with the examples partly depicted in <FIG>, <FIG>, <FIG>, an image processing method is used to determine the distance in pixels between the images of the laser spots displayed on the image <NUM>. The main goal of the image processing step is to determine the distance in pixels between laser spots on the target object. There are several methods that could be used for this, such as those using pixel color, but that approach is not very robust in environments that might have a wide variety of lighting conditions. For this application a process involving sequential images with the laser spots on in one image and off in the next is used. The method involves cycling the laser pointers on and off at a rate that is half the rate (or other integer divisor) of the video camera frame rate, and then performing an image subtraction step to identify contiguous clusters pixels that have changed. The difference between the centroids of the contiguous clusters will be the pixel distance (nPixels), which pixel distance can be used in conjunction with the distance d to calculate the scale factor and later display the scale indicator that graphically depicts that scale factor.

Change detection is a process used to determine the difference between two or more images. For example, regions of change can be determined using digital image processing techniques. One such process may involve image subtraction, blur filters, and image segmentation steps. The term "digital image processing" means a computer-based analysis of an image or series of images. The term "pixels" refers to picture elements that make up a <NUM>-D digital image. Segmentation is the process of identifying pixels that have similar properties in a digital image.

<FIG> is a diagram illustrating steps of a method for processing images to determine the distance in pixels between laser spots on a target object in accordance with one example. The video camera <NUM> captures respective frames 140a-d at times T = <NUM>, <NUM>, <NUM> and <NUM>. The laser pointers 132a and 132b are OFF when the frames 140a and 140c are captured at times T = <NUM> and <NUM>, but ON when the frames 140b and 140d are captured at times T = <NUM> and <NUM>. Frame 140b is subtracted from frame 140a to produce a subtraction image 142a; frame 140d is subtracted from frame 140c to produce a subtraction image 142b. The locations of differences in the subtraction images 142a and 142b are then determined. The centroid of each area is found and converted into pixel coordinates (x, y). A distortion correction is performed to compensate for the optics of the camera, where two-dimensional image corrections are applied resulting in (x', y'). This correction may depend on, for example, the lens optics, zoom, and focus levels. The corrections are determined experimentally in one example, and recalled at run-time using a table lookup. After lighting correction is applied, the differences (i.e., the images of the laser spots) which appear in each subtraction image are determined. In one example, a pixel-by-pixel difference operation is performed, followed by a blur filter operation, and then an image segmentation operation. An N x N blur filter (such as a <NUM> x <NUM> kernel) can be used to smooth out most of the high-frequency noise associated with the images, and can be adjusted to discard areas of various sizes. The blurred image is then segmented into distinct, non-touching areas. The centroid of each of the separate regions is computed and is stored in a list associated with each of the image pairs. The number of pixels separating the two centroids corresponding to the two laser spots is then calculated.

Since the groups of pixels representing the respective positions <NUM> and <NUM> of the laser spots will be in the same horizontal strip of each image, only that part of the image is needed for the image processing. <FIG> is a diagram illustrating steps of a method for processing images to determine the distance in pixels between laser spots on a target object in a manner which improves the image processing efficiency. Horizontal strips 144a and 144b, for example, can be created from by frames 140a and 140b respectively. Horizontal strip 144b is then subtracted from horizontal strip 144a to form a subtraction image <NUM>.

Another category of examples of the concept are configurations where UAV contains two or more laser range meters that enables: measurement of distance to the target, reference scale, as well as one or more orientation angle of the UAV relative to the target. If three non-collinearly mounted laser range meters are used (not shown here), more than one orientation angle can be measured (for example yaw and pitch).

<FIG> is a diagram showing a top view of an airborne UAV <NUM> that is capable of acquiring scale information for objects undergoing non-destructive inspection. It is also capable of measuring the separation distance D between the UAV <NUM> and the target object <NUM> and the orientation angle of the UAV <NUM> relative to target object <NUM>. The architecture of the UAV <NUM> depicted in <FIG> may be similar to the architecture depicted in <FIG>, except that a pair of laser range meters 138a and 138b arranged in a parallel configuration are substituted for the laser pointers <NUM>.

The UAV <NUM> depicted in <FIG> comprises a frame <NUM> and four rotors 124a-124d rotatably mounted to the frame <NUM>. Rotation of each rotor is driven by a respective motor (not shown in <FIG>) mounted to the frame <NUM>. The pair of laser range meters 138a and 138b having mutually parallel axes are fixedly mounted to the frame <NUM>. When activated, the laser range meters 138a and 138b direct respective mutually parallel laser beams at respective laser spots on a surface of a target object <NUM>. Although not shown in <FIG>, the UAV <NUM> also comprises a video camera <NUM> (see <FIG>) mounted to the frame <NUM>. Preferably the focal axis of the video camera <NUM> and the aim directions of the laser range meters 138a and 138b are mutually parallel.

In instances wherein the axes of the laser range meters 138a and 138b are not perpendicular to the portion of the surface of target object <NUM> where the laser beams impinge, the respective distances separating the laser range meters 138a and 138b from that surface will not be equal and the UAV <NUM> will have a non-zero orientation angle relative to that surface. In instances wherein the axes of the laser range meters 138a and 138b are perpendicular to the portion of the surface of target object <NUM> where the laser beams impinge, the respective distances separating the laser range meters 138a and 138b from that surface will be equal and the orientation angle will be zero. Thus measurements of the respective separation distances of the laser range meters 138a and 138b from the target object <NUM> can be used to determine the current offset of the UAV <NUM> from the target object <NUM> and the current orientation angle and then control the UAV <NUM> to move in a manner that reduces both the deviation of the current offset from a goal offset and the deviation of the current orientation angle from a target orientation angle (e.g., an angle of zero degrees).

The video camera <NUM> may be activated to capture an image in which the two laser spots are visible. This image data can be processed (as described in some detail below) to derive pixel information which, in conjunction with the known distance separating the axes of the two laser range meters 138a and 138b, can be used to determine the scale factor. That scale factor is then used to display a scale indicator on an image captured by the video camera <NUM> while the UAV is hovering at the same location.

For the multiple laser range meter examples, since the information associated with the distances to the target object <NUM> from the respective laser range meters has been measured, and since the field-of-view of the video camera <NUM> is known, it is possible to determine the scale factor without the need for the image processing step. The part that can be used from the image processing step is nPx, but that can be computed as a function of FoV, average distance D/n, L<NUM>, and maxPx (where n is the number of laser range meters) using the following equation: <MAT> (Note: The foregoing computation also needs an image distortion correction step, or more accurately the inverse of it.

In response to commands from the control station <NUM> (see <FIG>), the video camera <NUM> and the laser range meters 138a and 138b can be activated by control signals (e.g., via electrical cables) transmitted by the computer system <NUM>. The computer system <NUM> also controls the flight of the UAV <NUM> by sending commands to the motor controllers <NUM> which respectively control the rotation of respective motors <NUM> that drive rotation of rotors 124a-124d (see <FIG>).

The UAV <NUM> may comprises more than one laser range meter that enables measurement of distance to the target object, as well as one or more orientation angle. If two laser range meters are used (as in the example shown in <FIG>), one orientation angle can be measured (e.g., yaw). If three non-collinearly mounted laser range meters are used (not shown in the drawings), more than one orientation angle can be measured (e.g., yaw and pitch). From this information, a scale factor can be displayed to the user, or a motion constraint can be applied for vehicle control.

<FIG> is a flowchart identifying steps of a non-claimed method <NUM> for operating an unmanned aerial vehicle during non-destructive inspection of a structure where three non-collinearly mounted laser range meters are used and yaw and pitch orientation angles are measured. Method <NUM> comprises the following steps: (a) controlling an unmanned aerial vehicle to fly toward a structure to be inspected (step <NUM>); (b) using the three laser range meters on-board the unmanned aerial vehicle to repeatedly measure (i.e., calculate) respective distances separating the laser range meters from respective spots on a surface of the structure while the unmanned aerial vehicle is flying (step <NUM>); (c) calculating a first separation distance separating the unmanned aerial vehicle from the structure based at least on the distances calculated in step <NUM> (step <NUM>); (d) controlling the UAV to maintain a specified separation distance (e.g., equal to a goal offset) relative to the structure (step <NUM>); (e) computing yaw and pitch orientation angles of a focal axis of the camera relative to a plane intersecting the three laser spots on the surface of the structure based on the distances calculated in step <NUM> (step <NUM>); (f) controlling the unmanned aerial vehicle to reorient so that the focal axis of the camera is normal to the surface of the structure (step <NUM>); (g) using the camera on-board the unmanned aerial vehicle to capture an image of the structure while the unmanned aerial vehicle is hovering at the specified separation distance (e.g., at a first location) (step <NUM>); (h) calculating a scale factor for the image when displayed on the display screen based at least in part on the separation distance and a field of view of the camera (step <NUM>); (i) displaying the image with a scale indicator overlaid thereon, a value or a length of the scale indicator representing the scale factor (step <NUM>); and (j) determining with to continue the feedback control mode or not (step <NUM>). If a determination is made in step <NUM> that the feedback control mode should be continued, the process returns to step <NUM>. If a determination is made in step <NUM> that the feedback control mode should not continue, the prior UAV flight mode is resumed (step <NUM>).

In accordance with the configuration depicted in <FIG>, the data acquired by the equipment (i.e., the measurement data acquired by laser range meters 138a and 138b and the image data acquired by video camera <NUM>) on-board UAV <NUM> is transmitted by a transceiver <NUM>. That message is received by a control station <NUM> on the ground. The computer system at the control station <NUM> extracts the image data representing the image from the message and causes it to be displayed on the screen of display monitor <NUM> by controlling the states of the pixels of the display screen in accordance with the image data.

In accordance with one aspect of the motion control function, the UAV <NUM> can be controlled to translate to a second location while maintaining the separation distance. Then the video camera <NUM> is activated to capture a second image of the structure while the unmanned aerial vehicle is hovering at the second location, which second image can be displayed on the display screen. In some instances, the first and second images may respectively comprise first and second sets of image data representing partially overlapping or contiguous areas on a surface of the structure.

In accordance with another aspect of the motion control function, the computer system <NUM> may include a motion controller programmed to detect a deviation of the separation distance from the goal offset after the unmanned aerial vehicle has moved from the first location to a second location, and then control the unmanned aerial vehicle to fly to a third location at which the separation distance equals the goal offset, thereby reducing the deviation to zero. The motion controller may be further programmed to execute the following operations: computing an orientation angle of the focal axis of the camera relative to the surface of the structure based on the first, second and third distances; detecting a deviation from the desired orientation angle while the unmanned aerial vehicle is hovering at the first location; and controlling the unmanned aerial vehicle to change its orientation so that the orientation angle equals the desired orientation angle.

<FIG> shows a non-claimed system for inspecting structures in accordance with an alternative example. The depicted system includes a remote-controlled airborne UAV <NUM> that may be moved around a structure requiring periodic inspection. In this example, the UAV <NUM> is a rotorcraft and the structure to be inspected is a structural I-beam <NUM>. While the target structure is illustrated as a structural I-beam <NUM>, the system is equally well adapted for use in inspecting a wide range of other structures, including, but not limited to, power lines, power generating facilities, power grids, dams, levees, stadiums, large buildings, large antennas and telescopes, tanks, containers, water treatment facilities, oil refineries, chemical processing plants, high-rise buildings, and infrastructure associated with electric trains and monorail support structures. The system is also particularly well suited for use inside large buildings such as manufacturing facilities and warehouses.

In some examples, the UAV <NUM> can include an on-board system that is able to navigate the UAV <NUM> in accordance with a preprogrammed flight plan and to enable inspection data for the structural I-beam <NUM> to be acquired. In some examples, the UAV <NUM> can be flown along a flight path by an operator using a wireless UAV and payload controller <NUM> comprising a housing <NUM>, control user interface components <NUM>, a video display <NUM> and an antenna <NUM>. The inspection data acquired comprises image data captured by the video camera <NUM> and sensor data from one or more other sensors carried on-board the UAV <NUM>. The preprogrammed flight plan carried by UAV <NUM> enables the UAV <NUM> to follow a flight path to a location in proximity to the structural I-beam <NUM>. In some examples, more than one UAV <NUM> can be used to form a "swarm" of vehicles that can enable an inspection of various areas of a structure in less time than a single UAV.

The UAV <NUM> depicted in <FIG> comprises a frame <NUM> and four rotors 124a-124d rotatably mounted to the frame <NUM>. Rotation of each rotor is driven by a respective motor (not shown in <FIG>) mounted to the frame <NUM>. The UAV <NUM> further comprises an on-board local positioning system <NUM> mounted to the frame <NUM>. The local positioning system <NUM> comprises a pan-tilt mechanism <NUM>, a video camera <NUM> mounted to the pan-tilt mechanism <NUM>, and a laser range meter <NUM> affixed to the camera <NUM> in a manner such that the focal axis of the video camera <NUM> and the aim direction of the laser range meter <NUM> are mutually parallel. In the example depicted in <FIG>, the aim direction vector <NUM> of the laser range meter <NUM> is indicated by a dashed line, which dashed line also represents a laser beam transmitted by the laser range meter <NUM> and impinging on a surface of the structural I-beam <NUM> to form a laser spot <NUM>.

The video camera <NUM> may have automated (remotely controlled) zoom capabilities. The video camera <NUM> is supported on the pan-tilt mechanism <NUM>. The pan-tilt mechanism <NUM> comprises a pan unit <NUM> and a tilt unit <NUM>. The pan unit <NUM>, tilt unit <NUM>, video camera <NUM> and laser range meter <NUM> may be operated by an on-board computer system (not shown in <FIG>, but see computer system <NUM> in <FIG>). The computer system <NUM> in turn may be configured to receive commands from the wireless UAV and payload controller <NUM>, which may be operated by a technician on the ground.

<FIG> is a block diagram identifying some components of a non-claimed system for performing non-destructive inspection of a structure using a remote-controlled UAV <NUM> in accordance with an alternative example. In this example, the UAV <NUM> and the equipment carried by the UAV <NUM> are controlled by the computer system <NUM> as a function of radiofrequency commands transmitted by a control station <NUM>. Those radiofrequency commands are received by a transceiver <NUM> on-board the UAV <NUM>, converted into the proper digital format and then forwarded to the computer system <NUM>. The control station <NUM> may comprise a general-purpose computer system configured with programming for controlling operation of the UAV <NUM> and the equipment on-board the UAV <NUM>. For example, the pan and tilt angles of the pan-tilt mechanism <NUM>, and therefore the orientation of the video camera <NUM>, can be controlled using the keyboard, mouse, touchpad, or touchscreen of the computer system at the control station <NUM> or other user interface hardware (e.g., a gamepad). In addition, the computer system at the control station <NUM> is configured with programming for processing data received from the UAV <NUM> during an inspection operation. In particular, the computer system of the control station <NUM> may comprise a display processor configured with software for controlling a display monitor <NUM> to display images acquired by the video camera <NUM>. The optical image field, as sighted by the video camera <NUM>, can be displayed on the display monitor <NUM>.

As previously described, the equipment on-board the UAV <NUM> comprises a pan-tilt mechanism <NUM>, a video camera <NUM> and a laser range meter <NUM>, all of which can be activated by control signals (e.g., via electrical cables) transmitted by the computer system <NUM>. The computer system <NUM> also controls the flight of the UAV <NUM> by sending commands to the motor controllers <NUM> which respectively control the rotation of respective motors <NUM> that drive rotation of rotors 124a-124d (see <FIG>).

In accordance with one example, the pan-tilt mechanism <NUM> comprises a pan unit <NUM> (see <FIG>) configured to rotate the camera <NUM> (and laser range meter <NUM> mounted thereto) about a pan axis <NUM> and a tilt unit <NUM> (see <FIG>) configured to rotate the camera <NUM> (and laser range meter <NUM> mounted thereto) about a tilt axis, which is orthogonal to the pan axis, in response to control signals received from the computer system <NUM> (see <FIG>). Actuators (not shown in the drawings), such as servo-motors or the like, in the pan-tilt mechanism <NUM> may receive and respond to control signals from the computer system <NUM> by adjusting the angular rotation of the camera <NUM> about the pan and tilt axes, as well as the angular speed at which the camera <NUM>/laser range meter <NUM> rotate about the pan and tilt axes. The pan-tilt mechanism <NUM> further comprises pan and tilt rotational encoders (not shown in the drawings) that send signals representing current angular position data back to the computer system <NUM>. The control signals applied to the pan-tilt mechanism <NUM> may be computed by the computer system <NUM><NUM> in response to user instructions (e.g., manipulation of an input device that is part of the control station <NUM>) or an automatic path generator.

The pan-tilt mechanism <NUM> is controlled to rotationally adjust the laser range meter <NUM> and the video camera <NUM> to selected angles around the pan and tilt axes. The aim direction vector <NUM>, which describes the orientation of the laser range meter <NUM> (and the focal axis of the video camera <NUM>) relative to the fixed coordinate system of the frame <NUM> of UAV <NUM>, is determined from the pan and tilt angles when the laser range meter <NUM> is aimed at a point of interest on the structural I-beam <NUM>.

The laser range meter <NUM> may be incorporated inside the housing of video camera <NUM> or mounted to the outside of video camera <NUM> in such a way that it transmits a laser beam along the aim direction vector <NUM>. The laser range meter <NUM> is configured to measure the distance to any visible feature on or any marker attached to the structural I-beam <NUM>. In accordance with some examples, the laser range meter <NUM> uses a laser beam to determine the distance to the structural I-beam <NUM>. The most common form of laser range meter operates on the time-of-flight principle by sending a laser pulse in a narrow beam towards the structural I-beam <NUM> and measuring the time taken by the pulse to be reflected off the structural I-beam <NUM> and returned to a photodetector incorporated inside the laser range meter <NUM>. With the speed of light known and an accurate measurement of the time made, the distance from the laser range meter <NUM> to the laser spot <NUM> can be calculated. Many pulses are fired sequentially while the UAV <NUM> is hovering at a location and the average response is most commonly used.

Referring again to <FIG>, the equipment on-board the UAV <NUM> further comprises an inertial measurement unit <NUM> (hereafter "IMU <NUM>"). An inertial measurement unit works by detecting linear acceleration using one or more accelerometers and rotational rate using one or more gyroscopes. In a typical configuration, an inertial measurement unit comprises one accelerometer and one gyroscope per axis for each of the three vehicle axes: pitch, roll and yaw. The computer system <NUM> may further comprise a separate processor configured with inertial navigation software that utilizes the raw IMU measurements to calculate attitude, angular rates, linear velocity and position relative to a global reference frame. The data collected from the IMU <NUM> enables the computer system <NUM> to track the UAV's position using a method known as dead reckoning.

<FIG> is a diagram showing a top view of a non-claimed airborne UAV <NUM> having a local positioning system <NUM> comprising a video camera <NUM> and a laser range meter <NUM> directed at a target object <NUM>. The laser beam transmitted by the laser range meter <NUM> impinges on a surface of the target <NUM> at a laser spot <NUM>. The angle of the field-of-view <NUM> (indicated by a pair of dashed lines) of the video camera <NUM> is indicated by the arc labeled "ang" in <FIG>. The aim direction vector <NUM> extends from the laser range meter <NUM> to the laser spot <NUM> and has a length D (also referred to below as the "distance D" separating the laser range meter <NUM> and the target object <NUM>).

In accordance with one example, the distance D is measured by the laser range meter <NUM> while the angle of the field-of-view <NUM> is known. This information can be used to overlay or superimpose a size scale indicator on the screen of display monitor <NUM> (see <FIG>) when an image captured by the video camera <NUM> is being displayed. If the distance D to the target object <NUM> is known, scale information displayed in the image on the screen of display monitor <NUM> allows a user to gage the size of objects in the displayed image. The scale indicator could be in the form of the overall horizontal and vertical dimensions of the image on the display or an on-screen overlay showing scale factor data on a portion of the screen. This provides the size context for the scene captured by the video camera <NUM> and displayed in the image on the screen of display monitor <NUM>.

The known camera field-of-view angle is given by the following equation: <MAT> The image X and Y values are given by the following equations: <MAT> <MAT> where D is the distance to the target object surface measured by the laser range meter <NUM>, and "ratio" is the image aspect ratio (known), i.e., the ratio of the image width w to image height h.

In accordance with further examples, the fully motorized pan- tilt mechanism <NUM> can be used for aiming the laser range meter <NUM> independently of the UAV flight controls to acquire a direct measurement of the distance separating two points on the surface of the target object <NUM>. Assuming that the translational offset is zero or can be measured, then all of the basic features of the local positioning system <NUM> can be used.

In accordance with alternative examples, it may be possible using only a single powered and measured axis gimbal (tilt or pitch axis). For a UAV, the overall yaw (pan) associated with the vehicle can also be used to point the laser range meter <NUM> without changing vehicle position, but changing the pitch of the UAV <NUM> will cause the UAV <NUM> to translate. To address this, a separate motorized pitch controller for the laser range meter <NUM> can be used.

<FIG> is a flowchart identifying steps of a method <NUM> for sizing (i.e., measuring a point-to-point distance of) a feature on the surface of a structure to be inspected using a non-claimed UAV <NUM> carrying a local positioning system <NUM>. The method <NUM> comprises the following steps: (a) controlling the UAV <NUM> to fly toward and then hover at a first location which is separated from a structure to be inspected (step <NUM>); (b) aiming the laser range meter <NUM> at a first point corresponding to a first visible feature on a surface of the structure while the UAV is hovering at the first location (step <NUM>) and acquiring a first distance measurement (step <NUM>); (c) using the pan-tilt mechanism <NUM> to measure the respective pan and tilt angles of the laser range meter <NUM> when the latter is aimed at the first point (step <NUM>); (d) converting the distance and angle measurements acquired in steps <NUM> and <NUM> into a first vector representing the location of the first point in the frame of reference of the UAV <NUM> at the first location (step <NUM>); (e) aiming the laser range meter <NUM> at a second point corresponding to a second visible feature on the surface of the structure while the UAV <NUM> is hovering at a second location (step <NUM>) and acquiring a second distance measurement (step <NUM>); (f) using the pan-tilt mechanism <NUM> to measure the respective pan and tilt angles of the laser range meter <NUM> when the latter is aimed at the second point (step <NUM>); (g) converting the distance and angle measurements acquired in steps <NUM> and <NUM> into a second vector representing the location of the second point in the frame of reference of the UAV <NUM> at the second location (step <NUM>); (h) using an IMU <NUM> to measure acceleration and rotational rate of the UAV during flight from the first location to the second location (step <NUM>); (i) generating a transformation matrix representing a position difference and an orientation difference between the first and second locations of the UAV <NUM> based on information acquired in step <NUM> (step <NUM>); (j) multiplying the second vector by the transformation matrix to form a third vector representing the location of the second point in the frame of reference of the UAV <NUM> at the first location (step <NUM>); and (k) calculating a distance between the first and second points using the first and third vectors (step <NUM>).

In accordance with one example, the method described in the preceding paragraph further comprises: (l) transmitting one or more messages containing measurement data acquired in steps <NUM>, <NUM>, <NUM>, <NUM> and <NUM> from the UAV <NUM>; (m) receiving the one or more messages at a computer system at a ground station (e.g., control station <NUM> (see <FIG>)); and (n) extracting the measurement data from the message, wherein steps <NUM>, <NUM>, <NUM>, <NUM> and <NUM> are performed by the computer system at the ground workstation. This method may further comprise: using the video camera <NUM> to capture an image of a portion of the surface of the structure that includes the first and second visible features while the UAV is hovering at the first location; and displaying the image and symbology representing a value of the distance calculated in step <NUM> overlaid on the image on a display screen of the computer system at the ground workstation. For example, the first and second visible features may be respective endpoints of an anomaly (such as a crack) in the structure.

<FIG> is a vector diagram illustrating the above-described method for generating a vector representing the distance and direction from a first point on a target object to a second point on the target object using the above-described UAV <NUM>. Because a single laser range meter is used to directly measure coordinates for two points, a common reference location is used to determine the distance between the two points. In this situation, the user determines the difference between the first location of the UAV <NUM> during acquisition of the coordinates of the first point in a first frame of reference of the local positioning system <NUM> (and of the UAV <NUM>) and the second location of the UAV <NUM> during acquisition of the coordinates of the second point in a second frame of reference of the local positioning system which is offset from the first frame of reference. Using the acquired coordinate position data, a transformation matrix representing a position difference and an orientation difference between the first and second frames of reference of the local positioning system <NUM> (i.e., the differences between the first and second locations of the UAV <NUM> at the instants in time when the first and second measurements were made) is generated.

The vector diagram seen in <FIG> shows the configuration described in the preceding paragraph. Two pairs of mutually orthogonal arrows that meet at respective vertices graphically depict respective frames of reference (a respective third mutually orthogonal axis for each frame of reference is not shown to avoid clutter in the drawing). The left-hand pair of arrows represents a frame of reference A of the UAV <NUM> at the first location, while the right-hand pair of arrows represents a frame of reference B of the UAV <NUM> at the second location. The location offset of frame of reference B relative to frame of reference A is represented in <FIG> by the transformation matrix <MAT>, which is a <NUM>×<NUM> homogeneous transformation matrix that describes reference frame {B} relative to reference frame {A}. In this situation the position and orientation of reference frame {B} relative to reference frame {A} may be determined from data acquired by the IMU <NUM>.

The distance from the laser range meter <NUM> (not shown in <FIG>) to a first point P<NUM> on a surface of a target object <NUM> when the UAV <NUM> is at the first location is represented by the length of a vector AP<NUM> extending from the origin of frame of reference {A}. The distance from the laser range meter <NUM> to a second point P<NUM> on the surface of target object <NUM> when the UAV <NUM> is at the second location is represented by the length of a vector BP<NUM> extending from the origin of frame of reference {B} to second point P<NUM>. The vector BP<NUM> is then multiplied by the transformation matrix <MAT> to convert it into a vector defined in reference frame {A}. The resulting product is:<MAT> The magnitude (i.e., length) of vector AP<NUM> represents the distance from the laser range meter <NUM> to the second point P2 when the UAV <NUM> was at the first location. The distance d is determined from the difference between those two vectors, which operation can be expressed as follows: <MAT> In an equivalent manner, the distance d between points P<NUM> and P<NUM> is the magnitude (i.e., the Euclidean norm) of the <NUM>-D vector connecting the two points. It is computed as the square root of the sum of the squares of the differences of the individual components of the measured point coordinates (i.e., x, y and z values). The general form of this equation is: <MAT> The resulting distance value is displayed (e.g., superimposed or virtually overlaid) on the screen of the display monitor <NUM> along with the camera image of the portion of the surface of the target object <NUM> that includes points P1 and P2. Optionally, a line can be drawn between the two points to show context.

The flight of the UAV <NUM> during a non-destructive inspection operation may be subjected to various motion constraints which are designed to make the UAV <NUM> easier for a user to control for specific types of tasks. The term "motion constraints" should be given the ordinary kinematic definition. In general, motion constraints remove one or more degrees of freedom (DoF) from the motion of an object. For example, a single rigid body object in free space has six degrees of freedom (i.e., x, y, z, roll, pitch and yaw), but when that rigid body object is constrained, for example, by placing it on a table (in a location with gravity), the number of degrees of freedom is reduced to three (i.e., x, y and yaw). In this example, the planar surface of the table introduces motion constraints that remove three degrees of freedom from the system. In another example, if a rotational (revolute) joint is attached between a <NUM>-DoF object and another fixed-location object, the rotational joint constrains the motion of the object to one degree of freedom (i.e., rotation about the axis of the revolute joint), by removing five degrees of freedom from the system. These examples are physical motion constraints, but motion constraints can also be applied in software to remove one or more degrees of freedom from controlled motion - which is what is proposed in this disclosure.

For the system involving a UAV and its operator, which in standard operation can control six degrees of freedom in free space, the distance measurement information is used to constrain the motion of the UAV so that one or more of the degrees of freedom of the UAV is not directly available to the operator to control. For example, if a motion constraint is applied to the distance to the target object (using real-time measurement data from the laser range meter), the system will attempt to keep the UAV at that specified distance. This does not mean that the low-level controller cannot still control six degrees of freedom. Instead, it means that from the operator's point of view, there is one (or more) axis that they are not controlling directly. If a wind gust attempts to push the UAV in the direction of the motion constraint, the low-level controller will provide the motion control to compensate for this without requiring user input. This is useful in conditions where it is desirable to maintain a specific offset from a target object. It is also useful in providing virtual boundaries or for collision avoidance.

Once the measurement data has been acquired, it can be displayed to the user or used for additional capabilities, such as providing motion constraints that can be used for vehicle control. This extension enables motion control capabilities for the UAV <NUM> based on feedback of the data from the sensors and derived measurement data. This results in the ability to provide for semi-automated control to the system, as well as more intuitive manual control.

For the examples that employ laser pointers, the only types of motion constraints that can be added to the control system are those associated with position, since these examples do not measure orientation. The examples that have two or more laser range meters have the ability to measure orientation of the UAV <NUM> relative to the target object <NUM>, in addition to determining the distance. This allows the examples with more than one laser range meter to control both position and orientation of the UAV <NUM> relative to the target object <NUM>.

<FIG> is a block diagram identifying steps of a feedback control process <NUM> for controlling the motion of a vehicle <NUM> based on measurement data acquired by the equipment on-board the vehicle <NUM>. First, the user or agent inputs commands regarding the target distance and orientation of the vehicle <NUM> (step <NUM>), which inputs are received by a summing junction <NUM>. The summing junction <NUM> also receives distance and orientation data from a distance and orientation computation software module which is configured to compute distance and orientation (step <NUM>). The summing junction <NUM> subtracts the computed distance from the commanded distance and subtracts the computed orientation from the commanded orientation. The resulting deviations are output to a control signal computation software module that is configured to compute control signals calculated to reduce the deviations (step <NUM>). Based on the output from the summing junction <NUM>, the control signal computation software module outputs control signals to the motion actuators <NUM> (e.g., rotor motor controllers) on-board the vehicle <NUM>. During flight of the vehicle <NUM>, the sensors acquire sensor data (step <NUM>), which sensor data is used to compute the distance and orientation (step <NUM>).

In accordance with some examples, the computer system <NUM> uses an on-board alignment methodology to determine relative location (position and orientation) offsets of the video camera <NUM> relative to the target object <NUM>. This process uses distance information from three laser range meters to compute relative location in real-time. The computer system <NUM> then uses that data to produce the desired feedback-based motion of the UAV <NUM>.

One form of control that this process enables is semi-automated control to assist an operator in some aspect of alignment, such as orientation of the video camera <NUM> to make sure that its focal axis is always perpendicular to the surface of the target object or making sure that it is always a specific distance from the surface.

More specifically, the computer system <NUM> is configured (e.g., programmed) to determine what movements are needed to align the focal axis of the video camera <NUM> with a vector normal to the surface of the target object based on the distance information received from the laser range meters. The computer system <NUM> sends command signals to selected motor controllers <NUM> to activate the motors <NUM> as needed to orient the UAV <NUM> so that the focal axis of video camera <NUM> is aligned with the surface normal.

In addition to using the three laser range meters to determine distance to the target object, they are also used to determine the yaw and pitch orientation angles (hereinafter "yaw angle" and "pitch angle"). For the purpose of illustration, assume that the three laser range meters are disposed at the vertices of an isosceles triangle such that the distance separating the two laser range meters disposed at the vertices of the base of the isosceles triangle is a and the distance separating the third laser range meter and a midpoint of the base of the isosceles triangle (i.e., the height of the isosceles triangle) is b. Assume that d<NUM>, d<NUM>, and d<NUM> are the respective measured distances of the respective laser range meters to the surface of the target object. Equations (<NUM>) and (<NUM>) can be used to calculate the pitch and yaw angles: <MAT> <MAT> where PitchAngle and YawAngle are the current computed orientation angles relative to the surface of the target object, and atan2 is the two argument arctangent inverse trigonometric function. The goal for these angles, which are measured relative to the surface normal at the current location, is to be equal to zero; and the process to achieve the goal angles is described below.

With the current yaw and pitch angles calculated, the system motion controller can use a velocity control method for the controlled motions: pan, tilt, and distance. A feedback controller, such as a proportional-integral-derivative (PID) controller, can be used to drive to zero the error between the current angle and the desired angle. Equations (<NUM>) and (<NUM>) can be used to compute the pitch and yaw motion control: <MAT> <MAT> where PitchRate and YawRate describe the angular rotation rates about the pitch axis of the alignment apparatus and yaw axis of the base, respectively; Kppitch and Kpyaw are the proportional feedback gains associated with the pitch and yaw axes, respectively; PitchAngle and YawAngle are the angles computed from Eqs. (<NUM>) and (<NUM>), respectively; and PitchAnglegoal and YawAnglegoal are the desired goal angles to which the controller is driving the system toward (as mentioned earlier, these are both zero for this example). Integral and derivative feedback may also be used, but are not shown here.

While methods for controlling the operation of an unmanned aerial vehicle during non-destructive inspection of a structure have been described with reference to various examples, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the claims. In addition, many modifications may be made to adapt the teachings herein to a particular situation without departing from the scope of the attached claims. As used in the claims, the term "location" comprises position in a three-dimensional coordinate system and orientation relative to that coordinate system.

The methods described herein may be encoded as executable instructions embodied in a non-transitory tangible computer-readable storage medium, including, without limitation, a storage device and/or a memory device. Such instructions, when executed by a processing or computing system, cause the system device to perform at least a portion of the methods described herein.

Claim 1:
A method for operating an unmanned aerial vehicle for acquiring scale information for a structure undergoing non-destructive inspection, comprising:
(a) controlling an unmanned aerial vehicle (<NUM>) to fly toward a structure (<NUM>, <NUM>), wherein the unmanned aerial vehicle (<NUM>) is provided with on-board sensors and processing techniques to provide a scale factor usable for displaying a scale indicator on images captured by a video camera (<NUM>);
(b) using first and second laser pointers (132a, b) on-board the unmanned aerial vehicle (<NUM>) to repeatedly measure a distance (d, nPx) separating first and second spots (<NUM>, <NUM>), which are produced by the first and second laser pointers on the surface of the structure, from an image (<NUM>) of the first and second spots acquired by the video camera (<NUM>) while the unmanned aerial vehicle is flying, wherein a field-of-view of the camera (<NUM>) and aim directions (<NUM>) of the laser pointers (<NUM>) overlap;
(c) calculating a separation distance (D) separating the unmanned aerial vehicle from the structure based at least on the distance (d, nPx) separating the first and second spots (<NUM>, <NUM>);
(d) determining whether the separation distance (D) equals a goal offset;
(e) controlling the unmanned aerial vehicle to hover at a first location separated from the structure by the separation distance in response to a determination in step (d) that the separation distance is equal to the goal offset;
(f) using the camera (<NUM>) on-board the unmanned aerial vehicle to capture a first image of the structure while the unmanned aerial vehicle is hovering at the first location for
(g) displaying the first image on a display screen (<NUM>, <NUM>); and,
when the separation distance (D) is equal to the goal offset, the method further comprises:
calculating the scale factor for the first image when displayed on the display screen based at least in part on the separation distance and the field of view of the camera; wherein the scale indicator (<NUM>) is overlaid on the first image displayed on the display screen, a value or a length of the scale indicator representing the scale factor.