Title of Invention: AUTOMATIC REGISTRATION OF MULTIPLE MEASUREMENT DEVICES Abstract: A computer-implemented method is performed by one or more processors to automatically register a plurality of captured data obtained using a respective measurement device, each of the captured data is obtained separately. The computer-implemented method includes accessing a first captured data of a portion of an environment, and a first image corresponding to said portion of the environment captured from a known relative position and angle with respect to the first captured data. Further, from the plurality of captured data, a second captured data is identified that has at least a partial overlap with said portion, the second captured data is identified based on a corresponding second image. The second image is captured from a known relative position and angle with respect to the second captured data. The method further includes transforming the second captured data and/or the first captured data to a coordinate system. Detailed Description: CROSS REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application Ser. No. 63/131,594, filed Dec. 29, 2020, the entire disclosure of which is incorporated herein by reference. BACKGROUND The subject matter disclosed herein relates to use of a measurement devices, such as laser scanners, and performing automatic registration of data captured by multiple such measurement devices. Various applications such as facility management, forensic/crime scene investigation, accident reconstruction, architectural/civil engineering, and heritage documentation/restoration, use various types of measurement devices such as two-dimensional (2D), and three-dimensional (3D) laser scanners. For example, volume scanners are used to capture measurements of entire environments, such as crime scenes, building facades, or complex piping and wiring, and various other such cumbersome tasks. Measurement devices provide an economical way of capturing and analyzing millions (or more) of 3D data points in the environment to facilitate generating detailed 2D and/or 3D images of complex environments and geometries. In addition, measurement devices such as 3D images facilitate performing inspections and verify assemblies of products in an industrial setting accurately, and at relatively lesser cost. Measurement devices also include laser trackers that perform precise coordinate measuring that can facilitate industrial operations such as alignment, installation, part inspection, and other types of manufacturing and assembly integration projects. While existing measurement devices are suitable for their intended purposes, what is needed is a system having certain features of embodiments of the present disclosure. BRIEF DESCRIPTION According to one or more embodiments, a system includes one or more processors that automatically register a plurality of captured data, each of the scanned data is captured separately. A method for the registering includes accessing a first captured data of a portion of an environment, and a first image corresponding to the first captured data, the first image is of said portion of the environment captured from a known relative position and angle with respect to the first captured data. The method further includes extracting a set of features from the first image. The method further includes identifying, from the plurality of captured data, a second captured data that has at least a partial overlap with said portion, the second captured data is identified based on a corresponding second image including a subset of features from the set of features that is extracted from the first image, the second image is captured from a known relative position and angle with respect to the second captured data. The method further includes determining a pose and angle of the first image with respect to the second image by aligning the first image to the second image by using the subset of features that are in both, the first image and the second image. The method further includes transforming the second captured data and/or the first captured data to a coordinate system based on known the computed pose and angle and relative positions and angles of the first image and the second image and the first captured data and the second captured data, respectively. In one or more embodiments, the first image is captured at the time the first captured data is obtained. In one or more embodiments, the first captured data and the second captured data are obtained at different times. In one or more embodiments, the first captured data and the second captured data are captured using different types of measurement devices. In one or more embodiments, the first image and the second image are captured using different image capturing devices. In one or more embodiments, the first captured data comprises a 3D point cloud of said portion, and the second captured data comprises a 2D scan of said portion. In one or more embodiments, the set of features comprises one or more natural features that are detected in said portion. In one or more embodiments, the set of features comprises one or more artificial markers that are detected in said portion. In one or more embodiments, the first image and the second image are each captured using a respective wide-angle lens. In one or more embodiments, the coordinate system is that of the first captured data. In one or more embodiments, the plurality of captured data is stored in a database. In one or more embodiments, the pose and angle of the second captured data is determined further based on one or more sensor measurements associated with a measurement device that is used to capture the second captured data. According to an embodiment, a computer-implemented method is performed by one or more processors to automatically register a plurality of captured data obtained using a respective measurement device, each of the captured data is obtained separately. The computer-implemented method includes accessing a first captured data of a portion of an environment, and a first image corresponding to the first captured data, the first image is of said portion of the environment captured from a known relative position and angle with respect to the first captured data. The method further includes extracting a set of features from the first image. The method further includes identifying, from the plurality of captured data, a second captured data that has at least a partial overlap with said portion, the second captured data is identified based on a corresponding second image including a subset of features from the set of features that is extracted from the first image, the second image is captured from a known relative position and angle with respect to the second captured data. The method further includes determining a pose and angle of the first image with respect to the second image by aligning the first image to the second image by using the subset of features that are in both, the first image and the second image. The method further includes transforming the second captured data and/or the first captured data to a coordinate system based on known the computed pose and angle and relative positions and angles of the first image and the second image and the first captured data and the second captured data, respectively. Embodiments of the technical solutions described herein can include devices, apparatus, computer program products, and any other implementation of a machine, process, or a combination thereof. These and other advantages and features will become more apparent from the following description taken in conjunction with the drawings. BRIEF DESCRIPTION OF THE DRAWINGS The subject matter, which is regarded as the invention, is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: FIG. 1 depicts a system for capturing measurements in an environment according to one or more embodiments; FIG. 2 depicts a flowchart for a method 200 for automatically registering captured data from different data sources, and/or at different times according to one or more embodiments; FIG. 3 depicts an example data structure to store the captured data and the corresponding image according to one or more embodiments; FIGS. 4, 5, and 6 depict a laser scanner for optically scanning and measuring the environment surrounding the laser scanner; FIG. 7 shows a block diagram of elements of a laser scanner according to one or more embodiments; FIGS. 8A-8C depict an embodiment of a 2D scanner; FIGS. 9-11 depict an embodiment of a mobile scanning platform; FIGS. 12-14 depict an embodiment of a laser tracker device; FIG. 15 shows a block diagram of elements of a laser tracker device; FIGS. 16, 17, 18A, and 18B depict a handheld 3D imager; and FIG. 19 depicts a computer system according to one or more embodiments. The detailed description explains embodiments of the invention, together with advantages and features, by way of example with reference to the drawings. DETAILED DESCRIPTION Embodiments herein relate to automated linking or registering of data captured by a measuring device with existing data that has already been captured by the same measuring device, or another measuring device. For example, the present data may be captured by a laser scanner, while the existing data can include data captured using any other measuring device such as, a laser tracker, FARO® FREESTYLE®, FARO® SCANPLAN®, articulated arm, or any other type of measuring device. The existing data may have been captured at a different time. For example, the existing data can be older by a day, a week, a month, a year, or any other duration. The present data and the existing data can be stored in a cloud platform, such as using a distributed storage system, although any other storage architecture can be used without affecting the features of the technical solutions described herein. Embodiments of the technical solutions described herein facilitate using the captured data from these varied data sources, i.e., measurement devices, together. Embodiments of the technical solutions described herein facilitate determining the captured data that are to be registered together, and proceeding with such registering so that a user can obtain a holistic view of the environment, and at least the portion for which data was captured. Such processing, including determining the relevant captured data and their registration is performed automatically. There are several technical challenges with using the data from such varied data sources together. The technical challenges include identifying which two (or more) captured data are relevant for registering. The technical challenges further include that the captured data can be in different coordinate systems of the respective data sources. That is because the pose, i.e., position and orientation, of the respective measurement device can be different at the time of data capture. Embodiments of the technical solutions described herein address such technical challenges using images of the portions for which data is captured. The images can be captured by cameras associated with the measuring devices, for example, color cameras and/or infrared cameras. The cameras can use wide-angle or ultrawide-angle lenses in one or more embodiments. The cameras that capture the images can be internal to the measurement devices and/or external to the measurement devices. Embodiments of the present disclosure provide technical solutions to technical challenges in coordinate measurement devices. The measurement devices can capture two-dimensional or three-dimensional (3D) scans or measurements. Such measurements/scans can include 3D coordinates, 2D maps, 3D point clouds, or a combination thereof. The measurements/scans can include additional components, such as annotations, images, textures, measurements, and other details. A laser tracker device is a metrology device that measures positional coordinates using laser light. Laser tracker devices of the type discussed herein may be used in manufacturing environments where it is desired to measure objects, parts, or assemblies with a high level of accuracy. It should be appreciated in some applications, multiple laser tracker devices may be used and may be positioned in locations that are distant from an operator. An exemplary embodiment of a laser tracker system 20 is provided that allows an operator or user to control and operate the functions of a desired laser tracker device is illustrated in FIG. 12 (described later herein). The laser tracker system 1420 includes at least one laser tracker device 1422A, and may include a plurality of laser tracker devices 1422B-1422E. The system 1420 further includes at least one retroreflective target 1424A, and may include a plurality of retroreflective targets 1424B-1424D. As will be discussed in more detail herein, the retroreflective targets 1424A-1424D cooperate with laser light emitted by the laser tracker devices 1422A-1422E to allow a laser tracker device to measure the distance between the laser tracker device and the retroreflective target. With the distance to the retroreflective device determined, angular measurement devices, such as angular encoders for example, in the laser tracker device allow for the determination of the coordinates of the retroreflective device in a laser tracker device frame of reference. The system 1420 further includes a computer network 1426 that may include one or more nodes 1428, such as a computer server for example. The computer network 1426 may be any known computer network, such as but not limited to a local area network (LAN), a wide-area network (WAN), a cellular network or the Internet for example. In an embodiment, each of the laser tracker devices includes communications circuits, such as Ethernet (IEEE 802.3), WiFi (IEEE 802.11) or cellular communications circuits for example, that are configured to transmit to and receive signals from the computer network 1426. The system 1420 further includes at least one mobile computing device 30. As will be discussed in more detail herein, the mobile computing device 30 includes communications circuits that allow the mobile computing device 30 to transmit to and receive signals from the computer network. As will be discussed in more detail herein, the computer network 1426 allows the mobile computing device 30 to transmit signals to and receive signals from one or more of the laser tracker devices 1422A-1422E. As used herein, the term “mobile computing device” refers to a computing device having one or more processors, a display, and non-transitory memory that includes computer readable instructions. The mobile computing device also includes a power source, such as a battery for example, that allows a user to move about the environment with the mobile computing device. The mobile computing device is sized and shaped to be carried by a single person. In an embodiment, the mobile computing device may be but is not limited to a cellular phone, a smartphone, a personal digital assistant, a tablet computer, a laptop computer or a convertible laptop computer for example. Other types of coordinate measurement devices measure an area as opposed to discrete points, as is done in the laser tracker of FIG. 12. Because coordinate points for an area are being measured simultaneously, this measurement process is sometimes referred to as a “scan.” Typically, when capturing a scan of an environment, a version of the simultaneous localization and mapping (SLAM) algorithm is used. For completing such scans a scanner, such as the FARO® SCANPLAN®, FARO® SWIFT®, FARO® FREESTYLE®, or any other measurement system incrementally builds the scan of the environment, while the scanner is moving through the environment, and simultaneously the scanner tries to localize itself on this scan that is being generated. An example of a handheld scanner is described in U.S. patent application Ser. No. 15/713,931, the contents of which is incorporated by reference herein in its entirety. This type of scanner may also be combined with a another scanner, such as a time of flight scanner as is described in commonly owned U.S. patent application Ser. No. 16/567,575, the contents of which are incorporated by reference herein in its entirety. It should be noted that the scanners listed above are just examples of measurement devices and that the type of scanner used in one or more embodiments does not limit the features of the technical solutions described herein. FIG. 1 depicts a system for capturing measurements in an environment according to one or more embodiments. The measurement system 100 includes a computing system 110 coupled with a measurement device 120. The coupling facilitates wired and/or wireless communication between the computing system 110 and the measurement device 120. The measurement device 120 can include a laser tracker, a 2D scanner, a 3D scanner, or any other measurement device or a combination thereof. The captured data 125 from the measurement device 120 includes measurements of a portion from the environment. The captured data 125 is transmitted to the computing system 110 for storage. The computing device 110 can store the captured data 125 locally, i.e., in a storage device in the computing device 110 itself, or remotely, i.e., in a storage device that is part of another computing device 150. The computing device 150 can be a computer server, or any other type of computing device that facilitates remote storage and processing of the captured data 125. In one or more embodiments, the captured data 125 can be used to generate a map 130 of the environment in which the measurement device 120 is being moved. The computing device 110 and/or the computing device 150 can generate the map 130. The map 130 can be generated by combining several instances of the captured data 125, for example, submaps. Each submap can be generated using SLAM, which includes generating one or more submaps corresponding to one or more portions of the environment. The submaps are generated using the one or more sets of measurements from the sets of sensors 122. The submaps are further combined by the SLAM algorithm to generate the map 130. The captured data 125 can include one or more point clouds, distance of each point in the point cloud(s) from the measurement device 120, color information at each point, radiance information at each point, and other such sensor data captured by the set of sensors 122 that is equipped on the measurement device 120. For example, the sensors 122 can include a LIDAR 122A, a depth camera 122B, a camera 122C, etc. The measurement device 120 can also include an inertial measurement unit (IMU) 126 to keep track of a pose, including a 3D orientation, of the measurement device 120. Alternatively, or in addition, the captured data 125 the pose can be extrapolated by using the sensor data from sensors 122, the IMU 126, and/or from sensors besides the range finders. It should be noted that a “submap” is a representation of a portion of the environment and that the map 130 of the environment includes several such submaps “stitched” together. Stitching the maps together includes determining one or more landmarks on each submap that is captured and aligning and registering the submaps with each other to generate the map 130. In turn, generating each submap includes combining or stitching one or more sets of captured data 125 from the measurement device 120. Combining two or more captured data 125 requires matching, or registering one or more landmarks in the captured data 125 being combined. Here, a “landmark” is a feature that can be detected in the captured data 125, and which can be used to register a point from a first captured data 125 with a point from a second captured data 125 being combined. For example, the landmark can facilitate registering a 3D point cloud with another 3D point cloud or to register an image with another image. Here, the registration can be done by detecting the same landmark in the two captured data 125 (images, point clouds, etc.) that are to be registered with each other. A landmark can include, but is not limited to features such as a doorknob, a door, a lamp, a fire extinguisher, or any other such identification mark that is not moved during the scanning of the environment. The landmarks can also include stairs, windows, decorative items (e.g., plant, picture-frame, etc.), furniture, or any other such structural or stationary objects. In addition to such “naturally” occurring features, i.e., features that are already present in the environment being scanned, landmarks can also include “artificial” landmarks that are added by the operator of the measurement device 120. Such artificial landmarks can include identification marks that can be reliably captured and used by the measurement device 120. Examples of artificial landmarks can include predetermined markers, such as labels of known dimensions and patterns, e.g., a checkerboard pattern, a target sign, spheres, or other such preconfigured markers. In the case of some of the measurement devices 120, such as a volume scanner, the computing device 110, 150 can implement SLAM while building the scan to prevent the measurement device 120 from losing track of where it is by virtue of its motion uncertainty because there is no presence of an existing map of the environment (the map is being generated simultaneously). It should be noted that in the case of some types of the measurement devices 120, SLAM is not performed. For example, in the case of a laser tracker 20, the captured data 125 from the measurement device 120 is stored, without performing SLAM. FIG. 2 depicts a flowchart for a method 200 for automatically registering captured data from different data sources, and/or at different times according to one or more embodiments. The method 200 includes capturing and storing, by the measurement device 120, the captured data 125 for a portion of the environment, at block 202. The captured data 125 can be a 3D point cloud, a scan, a measurement (e.g., length of wall, dimensions of an object etc.), or any other such data captured by the measuring device 120. Further, an image is captured by the measurement device 120 from a known relative position and angle with respect to where the captured data 125 is obtained, at block 204. In one or more embodiments, it can be the same position as the first captured data 125. The image can be captured using a wide-angle or ultrawide-angle lens camera. The camera can be the camera 122C that is part of the sensors 122. Alternatively, the camera used for capturing the image can be an external camera attached to the measurement device 120. FIG. 3 depicts an example data structure to store the captured data and the corresponding image according to one or more embodiments. It should be noted that data structure 300 that is depicted is a table, however, in other embodiments the data structure can use another format, graphs, arrays, etc. The data structure 300 can be stored using a database system in one or more embodiments. Further, it should be noted that in other embodiments, the data structure 300 can include additional columns and rows that that are depicted. In the depicted data structure 300 includes another data structure 306 that is used to store the captured data 125. The data structure 306 is based on the type of the captured data 302. For example, a first data structure 306 for a first captured data 125 of type 302 “3D point cloud” will be different from a second data structure 306 that is used to store a second captured data 125 of type “laser measurement.” The data structure 300 further stores an identifier 304 of the measurement device 120 that is used to obtain the captured data 125. The identifier 304 can be a name, a serial number, a model number, or any other value that can facilitate identifying the measurement device 120. Further, the data structure 300 includes a timestamp 308 representing when the captured data 125 is obtained. Further, the data structure 300 includes an identifier image 310 that is captured of the portion for which the captured data 125 is obtained. For example, the first image that is captured in conjunction with the first captured data 125 is stored as the identifier image 310. The identifier image 310 can be an image captured using a wide-angle or ultrawide-angle lens in one or more embodiments. Further, in one or more embodiments, the identifier image 310 is of a predetermined dimension and resolution. Additionally, in one or more embodiments, the identifier image 310 is converted to a fingerprint 312 of the captured data 125. The fingerprint 312 can be a vectorized data that represents the identifier image 310, for example, a global image features like GIST features. Alternatively, a hash value of the identifier image 310 can be used as the fingerprint 312. Any other technique including parametric and nonparametric can be used to reduce the dimensionality of the identifier image 310 and store the result as the fingerprint 312. In some embodiments, other sensor measurements of the sensors 122 of the measurement device, such as the pose of the measurement device 120, if available are also stored. Accordingly, the data structure 300 is used to store the captured data 125 along with its metadata. In FIG. 3, each row represents a captured data 125 and its metadata. The data structure 300 can be used to the store the captured data 125 in the computing device 110 and/or the computing device 150. Referring to the flowchart, the method 200 further includes storing the captured data 125 and the image as its corresponding identifier image 310 in the data structure 300, at block 206. Further, a fingerprint 312 for the captured data 120 is computed using the identifier image 310, at block 208. The fingerprint 312 can be computed by a representation of low-dimensional image that contains enough information to identify the scene in an image. Such representations are for example, GIST or Convolutional Neural Network (CNN) features. In one or more embodiments, the fingerprint 312 can be based on one or more features that are extracted from the identifier image 310. The feature extraction can be performed using one or more algorithms, such as SIFT/SURF, or any other such algorithms. In response, an automated registering of the captured data 125 with one or more existing captured data 125 is performed in one or more embodiments. Alternatively, in some embodiments, the automated registration is triggered in response to an operator instructing to initiate such registration. For the automatic registration, the computing device 110 extracts a set of features from the identifier image 310 of the captured data 125, at block 210. The feature extraction can be performed using known techniques. The features that are extracted are detected based on landmarks or image features. The extracted features can include natural and/or artificial landmarks. For example, image feature extraction algorithms like Harris corner, SIFT/SURF, or any other such algorithms are used to extract one or more features from the identifier image 310. The feature extraction can be performed prior to the fingerprint computation in some embodiments, where the fingerprint 312 is computed based on the features extracted. Further, at block 212, another captured data 125 that is already stored in the data structure 300 is searched for, such that the other (i.e., second) captured data 125 has at least a partial overlap with the portion that is captured by the first captured data 125. The partial overlap is determined based on a second identifier image 310 of the second captured data 125. In one or more embodiments, extracted features from the first identifier image 310 and the second identifier image 310 are compared. If at least a predetermined number of the extracted features match, the first captured data 125 and the second captured data 125 are considered to have at least a partial overlap. Alternatively, or in addition, in one or more embodiments, a first fingerprint 312 of the first captured data 125 and a second fingerprint 312 of the second captured data are compared. If the match between the two is at least a predetermined level, the first captured data 125 and the second captured data 125 are considered to have at least a partial overlap. The feature matching is carried out by nearest neighbor search (NN). If a match satisfying the predetermined threshold is identified, the second captured data 125 that is determined is registered with the first captured data 125 (or vice versa) automatically. The registration includes determining a difference in pose and angle between the second captured data 125 and the first captured data 125, at block 214. The difference in pose and angle can be determined by using the subset of features that are in both, the first identifier image 310 and the second identifier image 310. Alternatively, if the angular information is available in the data structure 300, for example, based on the IMU of the respective measurement devices 120 used, the relative pose can be determined based on such angular information. Further, at block 216, a transformation is performed so that the first captured data 125 and the second captured data 125 are in a common coordinate system. The common coordinate system can be that of either the first captured data or the second captured data, or a third coordinate system altogether. The transformation changes the coordinates of all the data points in the captured data 125. In one or more embodiments, the transformation is also applied to the first identifier image 310 and the second identifier image 310. In order to perform such coordinate system transformation, the relative pos and angle of the image which is captured by 122 should be known with respect to the coordinate system of the measurement device 120. This information can be determined by a factory calibration and be known and available later during coordinate system transformation. At block 218, the first captured data 125 and the second captured data 125 are, after the transformation, aligned based on the matching features from the two captured data 125. Once aligned, the two captured data 125 can be used together. In one or more examples, the first captured data 125 and the second captured data 125 are captured using different measurement devices 120. For example, if the second captured data 125 includes dimension measurements of one or more objects in the field of view, the dimensions can be shown on a graphical user interface of the computing device 110, or the measurement device 120 overlaying the display of the first captured data 125. Alternatively, the first captured data 125 can be a 3D point cloud of a portion from a 3D scanner, and the second captured data 125 can be a 2D scan of the same portion from a 2D scanner. The 3D point cloud can be used to display details from the overlapping portion from the 2D scan. Further, the first captured data 125 and the second captured data 125 can be from different times. For example, the duration between the two captured data 125 can be a few minutes, a day, a week, a month, or any other duration. Alternatively, if the first captured data 125 and the second captured data 125 are both 3D point clouds, the two captured data 125 can be stitched together. Several other applications of using the two captured data 125 together can be performed. In one or more embodiments, the method 200 includes storing a link between the two captured data 125, at block 220. The link can include the transformation to align the two captured data 125. The link can be stored in the data structure 300 or in another data structure (not shown). In this manner, when an operator selects a captured data 125 that has other linked captured data 125, the operator can be shown that such linked captured data 125 exist. Further, a list of the linked captured data 125 can be shown to the operator for further examination or analysis if s/he so desires. The operations in the method 200 can be repeated until a matching data structure 125 is identified in the data structure 300. Further, various embodiments of different types of measurement devices 120 are shown. It should be noted that the further description of measurement devices 120 is not an exhaustive list and that any other type of measurement device 120 can also be used in implementation of the technical solutions described herein. Referring now to FIGS. 4-6, a laser scanner 20 is shown for optically scanning and measuring the environment surrounding the laser scanner 20. The laser scanner 20 has a measuring head 22 and a base 24. The measuring head 22 is mounted on the base 24 such that the laser scanner 20 may be rotated about a vertical axis 23. In one embodiment, the measuring head 22 includes a gimbal point 27 that is a center of rotation about the vertical axis 23 and a horizontal axis 25. The measuring head 22 has a rotary mirror 26, which may be rotated about the horizontal axis 25. The rotation about the vertical axis may be about the center of the base 24. The terms vertical axis and horizontal axis refer to the scanner in its normal upright position. It is possible to operate a 3D coordinate measurement device on its side or upside down, and so to avoid confusion, the terms azimuth axis and zenith axis may be substituted for the terms vertical axis and horizontal axis, respectively. The term pan axis or standing axis may also be used as an alternative to vertical axis. The measuring head 22 is further provided with an electromagnetic radiation emitter, such as light emitter 28, for example, that emits an emitted light beam 30. In one embodiment, the emitted light beam 30 is a coherent light beam such as a laser beam. The laser beam may have a wavelength range of approximately 300 to 1600 nanometers, for example 790 nanometers, 905 nanometers, 1550 nm, or less than 400 nanometers. It should be appreciated that other electromagnetic radiation beams having greater or smaller wavelengths may also be used. The emitted light beam 30 is amplitude or intensity modulated, for example, with a sinusoidal waveform or with a rectangular waveform. The emitted light beam 30 is emitted by the light emitter 28 onto a beam steering unit, such as mirror 26, where it is deflected to the environment. A reflected light beam 32 is reflected from the environment by an object 34. The reflected or scattered light is intercepted by the rotary mirror 26 and directed into a light receiver 36. The directions of the emitted light beam 30 and the reflected light beam 32 result from the angular positions of the rotary mirror 26 and the measuring head 22 about the axes 25 and 23, respectively. These angular positions in turn depend on the corresponding rotary drives or motors. Coupled to the light emitter 28 and the light receiver 36 is a controller 38. The controller 38 determines, for a multitude of measuring points X (FIG. 5), a corresponding number of distances d between the laser scanner 20 and the points X on object 34. The distance to a particular point X is determined based at least in part on the speed of light in air through which electromagnetic radiation propagates from the device to the object point X. In one embodiment the phase shift of modulation in light emitted by the laser scanner 20 and the point X is determined and evaluated to obtain a measured distance d. The speed of light in air depends on the properties of the air such as the air temperature, barometric pressure, relative humidity, and concentration of carbon dioxide. Such air properties influence the index of refraction n of the air. The speed of light in air is equal to the speed of light in vacuum c divided by the index of refraction. In other words, cair=c/n. A laser scanner of the type discussed herein is based on the time-of-flight (TOF) of the light in the air (the round-trip time for the light to travel from the device to the object and back to the device). Examples of TOF scanners include scanners that measure round trip time using the time interval between emitted and returning pulses (pulsed TOF scanners), scanners that modulate light sinusoidally and measure phase shift of the returning light (phase-based scanners), as well as many other types. A method of measuring distance based on the time-of-flight of light depends on the speed of light in air and is therefore easily distinguished from methods of measuring distance based on triangulation. Triangulation-based methods involve projecting light from a light source along a particular direction and then intercepting the light on a camera pixel along a particular direction. By knowing the distance between the camera and the projector and by matching a projected angle with a received angle, the method of triangulation enables the distance to the object to be determined based on one known length and two known angles of a triangle. The method of triangulation, therefore, does not directly depend on the speed of light in air. In one mode of operation, the scanning of the volume around the laser scanner 20 takes place by rotating the rotary mirror 26 relatively quickly about axis 25 while rotating the measuring head 22 relatively slowly about axis 23, thereby moving the assembly in a spiral pattern. In an exemplary embodiment, the rotary mirror rotates at a maximum speed of 5820 revolutions per minute. For such a scan, the gimbal point 27 defines the origin of the local stationary reference system. The base 24 rests in this local stationary reference system. In addition to measuring a distance d from the gimbal point 27 to an object point X, the scanner 20 may also collect gray-scale information related to the received intensity (equivalent to the term “brightness” or “optical power”) value. The gray-scale value may be determined at least in part, for example, by integration of the bandpass-filtered and amplified signal in the light receiver 36 over a measuring period attributed to the object point X. As will be discussed in more detail herein, the intensity value may be used to enhance color images that are used to colorize the scanned data. The measuring head 22 may include a display device 40 integrated into the laser scanner 20. The display device 40 may include a graphical touch screen 41, as shown in FIG. 1, which allows the operator to set the parameters or initiate the operation of the laser scanner 20. For example, the screen 41 may have a user interface that allows the operator to provide measurement instructions to the device, and the screen may also display measurement results. The laser scanner 20 includes a carrying structure 42 that provides a frame for the measuring head 22 and a platform for attaching the components of the laser scanner 20. In one embodiment, the carrying structure 42 is made from a metal such as aluminum. The carrying structure 42 includes a traverse member 44 having a pair of walls 46, 48 on opposing ends. The walls 46, 48 are parallel to each other and extend in a direction opposite the base 24. Shells 50, 52 are coupled to the walls 46, 48 and cover the components of the laser scanner 20. In the exemplary embodiment, the shells 50, 52 are made from a plastic material, such as polycarbonate or polyethylene for example. The shells 50, 52 cooperate with the walls 46, 48 to form a housing for the laser scanner 20. On an end of the shells 50, 52 opposite the walls 46, 48 a pair of yokes 54, 56 are arranged to partially cover the respective shells 50, 52. In the exemplary embodiment, the yokes 54, 56 are made from a suitably durable material, such as aluminum for example, that assists in protecting the shells 50, 52 during transport and operation. The yokes 54, 56 each includes a first arm portion 58 that is coupled, such as with a fastener for example, to the traverse 44 adjacent the base 24. The arm portion 58 for each yoke 54, 56 extends from the traverse 44 obliquely to an outer corner of the respective shell 50, 52. From the outer corner of the shell, the yokes 54, 56 extend along the side edge of the shell to an opposite outer corner of the shell. Each yoke 54, 56 further includes a second arm portion that extends obliquely to the walls 46, 48. It should be appreciated that the yokes 54, 56 may be coupled to the traverse 42, the walls 46, 48 and the shells 50, 54 at multiple locations. The pair of yokes 54, 56 cooperate to circumscribe a convex space within which the two shells 50, 52 are arranged. In the exemplary embodiment, the yokes 54, 56 cooperate to cover all of the outer edges of the shells 50, 54, while the top and bottom arm portions project over at least a portion of the top and bottom edges of the shells 50, 52. This provides advantages in protecting the shells 50, 52 and the measuring head 22 from damage during transportation and operation. In other embodiments, the yokes 54, 56 may include additional features, such as handles to facilitate the carrying of the laser scanner 20 or attachment points for accessories for example. On top of the traverse 44, a prism 60 is provided. The prism extends parallel to the walls 46, 48. In the exemplary embodiment, the prism 60 is integrally formed as part of the carrying structure 42. In other embodiments, the prism 60 is a separate component that is coupled to the traverse 44. When the mirror 26 rotates, during each rotation the mirror 26 directs the emitted light beam 30 onto the traverse 44 and the prism 60. Due to non-linearities in the electronic components, for example in the light receiver 36, the measured distances d may depend on signal strength, which may be measured in optical power entering the scanner or optical power entering optical detectors within the light receiver 36, for example. In an embodiment, a distance correction is stored in the scanner as a function (possibly a nonlinear function) of distance to a measured point and optical power (generally unscaled quantity of light power sometimes referred to as “brightness”) returned from the measured point and sent to an optical detector in the light receiver 36. Since the prism 60 is at a known distance from the gimbal point 27, the measured optical power level of light reflected by the prism 60 may be used to correct distance measurements for other measured points, thereby allowing for compensation to correct for the effects of environmental variables such as temperature. In the exemplary embodiment, the resulting correction of distance is performed by the controller 38. In an embodiment, the base 24 is coupled to a swivel assembly (not shown) such as that described in commonly owned U.S. Pat. No. 8,705,012 ('012), which is incorporated by reference herein. The swivel assembly is housed within the carrying structure 42 and includes a motor 138 that is configured to rotate the measuring head 22 about the axis 23. In an embodiment, the angular/rotational position of the measuring head 22 about the axis 23 is measured by angular encoder 134. An auxiliary image acquisition device 66 may be a device that captures and measures a parameter associated with the scanned area or the scanned object and provides a signal representing the measured quantities over an image acquisition area. The auxiliary image acquisition device 66 may be, but is not limited to, a pyrometer, a thermal imager, an ionizing radiation detector, or a millimeter-wave detector. In an embodiment, the auxiliary image acquisition device 66 is a color camera with an ultrawide-angle lens, sometimes referred to as a “fisheye camera.” In an embodiment, the camera 66 is located internally to the scanner (see FIG. 3) and may have the same optical axis as the 3D scanner device. In this embodiment, the camera 66 is integrated into the measuring head 22 and arranged to acquire images along the same optical pathway as emitted light beam 30 and reflected light beam 32. In this embodiment, the light from the light emitter 28 reflects off a fixed mirror 116 and travels to dichroic beam-splitter 118 that reflects the light 117 from the light emitter 28 onto the rotary mirror 26. In an embodiment, the mirror 26 is rotated by a motor 136 and the angular/rotational position of the mirror is measured by angular encoder 134. The dichroic beam-splitter 118 allows light to pass through at wavelengths different than the wavelength of light 117. For example, the light emitter 28 may be a near infrared laser light (for example, light at wavelengths of 780 nm or 1150 nm), with the dichroic beam-splitter 118 configured to reflect the infrared laser light while allowing visible light (e.g., wavelengths of 400 to 700 nm) to transmit through. In other embodiments, the determination of whether the light passes through the beam-splitter 118 or is reflected depends on the polarization of the light. The camera 66 obtains 2D images of the scanned area to capture color data to add to the captured point cloud. In the case of a built-in color camera having an optical axis coincident with that of the 3D scanning device, the direction of the camera view may be easily obtained by simply adjusting the steering mechanisms of the scanner—for example, by adjusting the azimuth angle about the axis 23 and by steering the mirror 26 about the axis 25. Referring now to FIG. 7 with continuing reference to FIGS. 4-6, elements are shown of the laser scanner 20. Controller 38 is a suitable electronic device capable of accepting data and instructions, executing the instructions to process the data, and presenting the results. The controller 38 includes one or more processing elements 122. The processors may be microprocessors, field programmable gate arrays (FPGAs), digital signal processors (DSPs), and generally any device capable of performing computing functions. The one or more processors 121 have access to memory 125 for storing information. Controller 38 is capable of converting the analog voltage or current level provided by light receiver 36 into a digital signal to determine a distance from the laser scanner 20 to an object in the environment. Controller 38 uses the digital signals that act as input to various processes for controlling the laser scanner 20. The digital signals represent one or more laser scanner 20 data including but not limited to distance to an object, images of the environment, images acquired by panoramic camera 66, angular/rotational measurements by a first or azimuth encoder 132, and angular/rotational measurements by a second axis or zenith encoder 134. In general, controller 38 accepts data from encoders 132, 134, light receiver 36, light source 28, and panoramic camera 66 and is given certain instructions for the purpose of generating a 3D point cloud of a scanned environment. Controller 38 provides operating signals to the light source 28, light receiver 36, panoramic camera 66, zenith motor 136 and azimuth motor 138. The controller 38 compares the operational parameters to predetermined variances and if the predetermined variance is exceeded, generates a signal that alerts an operator to a condition. The data received by the controller 38 may be displayed on a user interface 40 coupled to controller 38. The user interface 40 may be one or more LEDs (light-emitting diodes) 82, an LCD (liquid-crystal diode) display, a CRT (cathode ray tube) display, a touchscreen display or the like. A keypad may also be coupled to the user interface for providing data input to controller 38. In one embodiment, the user interface is arranged or executed on a mobile computing device that is coupled for communication, such as via a wired or wireless communications medium (e.g. Ethernet, serial, USB, Bluetooth™ or WiFi) for example, to the laser scanner 20. The controller 38 may also be coupled to external computer networks such as a local area network (LAN) and the Internet. A LAN interconnects one or more remote computers, which are configured to communicate with controller 38 using a well-known computer communications protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol), RS-232, ModBus, and the like. Additional systems 20 may also be connected to LAN with the controllers 38 in each of these systems 20 being configured to send and receive data to and from remote computers and other systems 20. The LAN may be connected to the Internet. This connection allows controller 38 to communicate with one or more remote computers connected to the Internet. The processors 121 are coupled to memory 125. The memory 125 may include random access memory (RAM) device 140, a non-volatile memory (NVM) device 142, and a read-only memory (ROM) device 144. In addition, the processors 121 may be connected to one or more input/output (I/O) controllers 146 and a communications circuit 148. In an embodiment, the communications circuit 92 provides an interface that allows wireless or wired communication with one or more external devices or networks, such as the LAN discussed above. Controller 38 includes operation control methods described herein, which can be embodied in application code. For example, these methods are embodied in computer instructions written to be executed by processors 121, typically in the form of software. The software can be encoded in any language, including, but not limited to, assembly language, VHDL (Verilog Hardware Description Language), VHSIC HDL (Very High Speed IC Hardware Description Language), Fortran (formula translation), C, C++, C#,
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