System and method for calibrating a head-mounted display device

Abstract

Disclosed herein is a method for calibrating a head mounted display (HMD) device. The method includes determining that a calibration error in the HMD device exceeds a predefined threshold value. In addition, the method includes a step of identifying a display device from a list of display devices available in the real world and within a field of view of the HMD. The method also includes providing a calibration pattern to the identified display device and providing a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device (102). The method also includes obtaining images of the calibration pattern and HMD motion data along the guidance path and corresponding guided head gestures performed by the user. Finally, the method includes performing the calibration of HMD based on HMD motion data and images.

Description

SYSTEM AND METHOD FOR CALIBRATING A HEAD-MOUNTED DISPLAY DEVICE

[0001] The present disclosure relates to calibration and more particularly, relates to a system and a method for calibrating a head-mounted display (HMD) device.

[0002] Head-mounted display (HMD) devices, such as Virtual Reality (AR) headsets or Video See Through (VST) devices, also known as Augmented Reality (AR) headsets are wearable computing devices that provide an immersive experience by placing a screen or display directly in front of a user’s eyes. The HMD devices generally include sensors to track head movements, allowing users to engage with digital environments in a more natural and intuitive way. Such sensors may include cameras, Light Detection and Ranging (LIDAR) sensors, Infrared (IR) sensors, and Inertial Measurement Unit (IMU) sensors mounted on the front of the HMD devices. The sensors of the HMD devices are calibrated at the time of manufacturing using calibration parameters. The calibration parameters specify the inherent sensor information and relative positions of each sensor.

[0003] Generally, calibration errors may arise due to various reasons, such as vibration caused during the transportation of the HMD devices and / or improper handling of the HMD devices. Such calibration errors may affect the performance of the HMD devices, thereby leading to incorrect perception of displayed images. One of the ways to mitigate this issue is to perform correction in calibration errors. However, such techniques can only correct minor calibration errors and are unable to correct major calibration errors.

[0004] Therefore, in view of the above-mentioned problems, it is advantageous to provide an improved system and method that can overcome the above-mentioned problems and limitations associated with the existing calibration techniques.

[0005] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the present disclosure. This summary is neither intended to identify key or essential inventive concepts of the present disclosure nor is it intended for determining the scope of the present disclosure.

[0006] According to an embodiment of the present disclosure, disclosed herein is a method for calibrating a head-mounted display (HMD) device. The method includes determining that a calibration error in the HMD exceeds a predefined threshold value. In addition, the method includes identifying, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD. The method also includes providing a calibration pattern to the identified at least one display device for displaying the calibration pattern. Further, the method includes providing a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD, such that the user guidance is indicative of the movement of a user along a guidance path and guided head gestures to be performed by the user along the guidance path in the real world. In addition, the method includes obtaining at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and for each of the corresponding guided head gestures performed by the user, respectively. Finally, the method includes performing the calibration of HMD based on at least one of the HMD motion data and the at least one image.

[0007] According to an embodiment of the present disclosure, disclosed herein is a system for calibrating a head-mounted display (HMD) device. The system includes a memory; and at least one processor in communication with the memory. The at least one processor is configured to determine that a calibration error in the HMD exceeds a predefined threshold value. In addition, the at least one processor is configured to identify, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD. The at least one processor is also configured to provide a calibration pattern to the identified at least one display device for displaying the calibration pattern. In addition, the at least one processor is configured to provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD, such that the user guidance is indicative of the movement of a user along a guidance path and guided head gestures to be performed by the user along the guidance path in the real world. In addition, the at least one processor is configured to obtain at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and for each of the corresponding guided head gestures performed by the user, respectively. Finally, the at least one processor is configured to perform the calibration of HMD based on at least one of the HMD motion data and the at least one image.

[0008] According to an embodiment of the present disclosure, a computer-readable storage medium storing instructions is provided. The instructions, when executed by the at least one processor, may cause the at least one processor to determine that a calibration error in the HMD device exceeds a predefined threshold value. The instructions, when executed by the at least one processor, may cause the at least one processor to identify, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device based on at least one selection parameter. The instructions, when executed by the at least one processor, may cause the at least one processor to provide a calibration pattern to the identified at least one display device for displaying the calibration pattern. The instructions, when executed by the at least one processor, may cause the at least one processor to provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device, wherein the user guidance is indicative of a movement of a user along a guidance path and head gestures in the real world to perform the calibration. The instructions, when executed by the at least one processor, may cause the at least one processor to obtain at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and corresponding head gestures, respectively. The instructions, when executed by the at least one processor, may cause the at least one processor to perform the calibration of the HMD device based on at least one of the HMD motion data and the at least one image.

[0009] To further clarify the advantages and features of the present present disclosure, a more particular description of the present disclosure will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawing. It is appreciated that these drawings depict only typical embodiments of the present disclosure and are therefore not to be considered limiting its scope. The present disclosure will be described and explained with additional specificity and detail with the accompanying drawings.

[0010] The foregoing and other features of embodiments will become more apparent from the following detailed description of embodiments when read in conjunction with the accompanying drawings. In the drawings, like reference numerals refer to like elements.

[0011] Figure 1 illustrates a schematic block diagram of a Head-mounted Display (HMD) device in communication with a system for calibrating the HMD device, in accordance with an embodiment of the present disclosure;

[0012] Figure 2 illustrates a detailed schematic block diagram of the system, in accordance with an embodiment of the present disclosure;

[0013] Figure 3 illustrates a schematic showing interaction of a calibration module with other modules of the system, in accordance with an embodiment of the present disclosure;

[0014] Figure 4 illustrates a schematic showing the interaction of an external display interface module with other modules of the system, in accordance with an embodiment of the present disclosure;

[0015] Figure 5 illustrates a schematic showing the interaction of a display selector module with other modules of the system, in accordance with an embodiment of the present disclosure;

[0016] Figure 6 illustrates a schematic showing the interaction of an object detection module with other modules of the system, in accordance with an embodiment of the present disclosure;

[0017] Figure 7 illustrates a schematic showing the interaction of a pattern generation module with other modules of the system, in accordance with an embodiment of the present disclosure;

[0018] Figure 8 illustrates a schematic showing the interaction of a gesture generation module with other modules of the system, in accordance with an embodiment of the present disclosure;

[0019] Figure 9 illustrates a flow chart of a method for calibrating the HMD device, in accordance with an embodiment of the present disclosure; and

[0020] Figures 10A and 10B illustrate a flow chart of another method for calibrating the HMD device in real-time, in accordance with an embodiment of the present disclosure;

[0021] Figure 11 illustrates a schematic showing the interactions of various modules of the system, in accordance with an embodiment of the present disclosure;

[0022] Figure 12 illustrates a video feed presented to the user showing markers, in accordance with an implementation of the present disclosure;

[0023] Figure 13 which illustrates a video feed presented to the user showing a calibration pattern, in accordance with an implantation of the present disclosure; and

[0024] Figures 14A and 14B illustrate a video feed showing a user guidance and user gestures, in accordance with an embodiment of the present disclosure.

[0025] For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the present disclosure relates.

[0026] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.

[0027] Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element do not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more…” or “one or more elements is required.”

[0028] Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and / or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and / or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.

[0029] Use of the phrases and / or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and / or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and / or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and / or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and / or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.

[0030] Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.

[0031] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0032] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

[0033] For the sake of clarity, the first digit of a reference numeral of each component of the present disclosure is indicative of the Figure number, in which the corresponding component is shown. For example, reference numerals starting with digit “1” are shown at least in Figure 1. Similarly, reference numerals starting with digit “2” are shown at least in Figure 2.

[0034] Figure 1 illustrates a schematic block diagram of a system 100 for calibrating a Head-mounted Display (HMD) device 102, in accordance with an embodiment of the present disclosure. The HMD device 102, in one example, may be a Video See Through (VST) device, also known as an Augmented Reality (AR) headset. The HMD device 102 may include a plurality of different sensors that allow the HMD device 102 to provide an immersive experience in the real world around the HMD device 102. The HMD device 102 may include sensors, but is not limited to, imaging sensors, infrared sensors, and Light Detection and Ranging (LIDAR) sensors. The imaging sensor may either be a 2-dimensional (2D) camera or a depth sensing camera or a combination thereof. Further, the sensors of the HMD device 102 are calibrated during its manufacturing.

[0035] The calibration in the HMD device 102 may be a process of aligning the virtual elements with the real world to ensure that they appear correctly and interact accurately with the real world. Further, calibration of sensors in the HMD device 102 involves adjusting and fine-tuning the sensors both with respect to the real world and with each other to ensure that the sensors accurately capture and interpret data from the real world. However, the transportation and / or usage of the HMD device 102 may cause dislocation of the sensor, thereby resulting in calibration error. For instance, the HMD 102 may accidentally slip from the user’s hands and the resulting fall may dislocate the position of imaging sensors. As a result, the image / video captured by the dislocated imaging sensor may be misaligned with data from the other sensors.

[0036] The system 100 of the present disclosure may be configured to rectify calibration errors that may arise after the manufacturing of the HMD device 102, i.e., during transportation and usage. The system 100 may rely on the real world and display devices in the real world. Such an approach allows the calibration without a need for a trained personnel. Further, the system 100 may allow the user to accurately rectify minor and major calibration errors. Moreover, the system 100 may monitor the HMD device 102 in real-time to check for the calibration error and prompt the user to initiate the rectification of the calibration error. Such an approach also ensures that the user does not experience dizziness caused by the calibration error. In one example, the system 100 may be positioned inside the HMD device 102 or in another example, the system 100 may be an external entity in communication with the HMD device 102 or the system 100 may be mounted on an HMD device 102. A detailed structure of the system 100 and an operation thereof is explained in subsequent paragraphs.

[0037] Figure 2 illustrates a detailed schematic block diagram of the system 100, in accordance with an embodiment of the present disclosure. The system 100 may include different components that operate synergistically to calibrate sensors of the HMD device 102. For instance, the system 100 may include a processor 202, a memory 204, module(s) 206, and data 208. The memory 204, in one example, may store the instructions to carry out the operations of the modules 206. The modules 206 and the memory 204 may be coupled to the processor 202.

[0038] The processor 202 can be a single processing unit or several units, all of which could include multiple computing units. The processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processor, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 202 is configured to fetch and execute computer-readable instructions and data stored in the memory 204.

[0039] The memory 204 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory 204, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0040] The module(s) 206, amongst other things, includes routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The modules 206 may also be implemented as, signal processor 202(s), state machine(s), logic circuitries, and / or any other device or component that manipulated signals based on operational instructions.

[0041] Further, the modules 206 can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, a processor, such as the processor 202, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor 202 which executes instructions to cause the general-purpose processor 202 to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules 206 may be machine-readable instructions (software) which, when executed by a processor 202 / processing unit, perform any of the described functionalities. Further, the data serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules 206. The data 208 may include information and / or instructions to perform activities by the processor 202.

[0042] The module(s) 206 may perform different functionalities which may include, but may not be limited to, receiving information and denoising the image frame. Accordingly, the module(s) 206 may include a calibration module 210, a display interface module 212, a display selection module 214, an object detection module 216, a pattern generation module 218, and a gesture generation module 220. In one example, the at least one processor 202 may configured to operate by actuating the aforementioned module(s) 206.

[0043] Details of the calibration module 210 are explained in conjunction with Figure 3 which illustrates a schematic showing interaction of the calibration module 210 with other modules of the system. The calibration module 210 may be adapted to calibrate the HMD device 102. The calibration module 210, in one example, may monitor the calibration error and compare the error with a threshold value. In case the calibration error exceeds the threshold value, the calibration module 210 may determine that the rectification of the calibration error or in other words, re-calibration of the HMD device 102 is needed. In one example, the threshold value may be understood as a degree of mismatch between the data generated by the sensor in real-time and data generated for the same action during the initial calibration at the time of manufacturing. The calibration module 210 may determine the misalignment by continuously monitoring the sensor data. Such an approach allows the calibration module 210 to promptly detect even the smallest calibration error which is otherwise not immediately perceived by the user and perform subsequent calibration. A manner in which the calibration module 210 operates is explained later with respect to Figures 5 and 6.

[0044] Details of the external display interface module 212 are explained in conjunction with Figure 4 which illustrates a schematic showing the interaction of the external display interface module 212 with other modules of the system. The display interface module 212 may be in communication with the calibration module 210 and may identify a plurality of display devices that can be used to calibrate the HMD device 102. The display interface module 212 may operate in response to a request made by the calibration module 210 when the calibration module 210 determines that the calibration error exceeds the predefined threshold value. The display interface module 212 may identify the display devices based on various selection parameters. Based on one of the selection parameters, the display interface module 212 may identify the display devices that are registered to the same user account as the user account to which the HMD device 102 is registered. Based on another selection parameter, the display interface module 212 may identify the display devices that are present in the real world. Based on another selection parameter, the display interface module 212 may identify the display device connected to the network as the HMD device 102. In addition, the display interface module 212 may collect the information about the display devices identified based on at least one selection parameter. The information may include but is not limited to, the type, size, and location of the display device. Further, the display interface module 212 may relay the list of the display devices and associated information to the display selection module 214.

[0045] The details of the display selection module 214 are explained in conjunction with Figure 5 which illustrates a schematic showing the interaction of the display selection module 214 interface module with other modules of the system. The display selection module 214 may be communicably coupled to the calibration module 210 and the display interface module 212 and may be adapted to select a display device out of the list of display devices shared by the display interface module 212. The display selection module 214 may select the display device based on at least one selection parameter. The at least one selection parameter may include, but is not limited to, a display device connected to the same network as the HMD device 102 and the dimensions of the display device. In addition, the display selection module 214 may receive the information on the display devices that are in the vicinity of the HMD device 102 from the object detection module 216. The display selection module 214 may be configured in such a way that the display selection module 214 may select the display device which has a suitable display size and is easily accessible to the user. The display selection module 214 may also provide recommendations to the user in case multiple display devices are eligible for the calibration and may receive a selection by the user via the HMD device 102.

[0046] The details of the object detection module 216 are explained in conjunction with Figure 6 which illustrates a schematic showing the interaction of the object detection module 216 interface module with other modules of the system. The object detection module 216 may process the image and video feeds from imaging sensors of the HMD device 102 to determine the object in the vicinity of the HMD device 102. In one example, the vicinity of the HMD device 102 may be a region within a Field-Of-View (FOV) of the HMD device 102. In addition, the vicinity may include additional regions around the user which can be scanned by moving around the HMD device 102 worn by the user. The object detection module 216 may use techniques such as Simultaneous Localization and Mapping (SLAM) to map the region around the user and based on the mapping, may identify objects in the region. In addition, the object detection module 216 may determine light conditions in the region. The purpose of determining the light conditions is to ascertain that sufficient light is available for the user to walk in case the user is prompted to walk in the region for the purpose of calibration. The determined object including the detected display device and light conditions may be communicated to the display selection module 214.

[0047] The details of the pattern generation module 218 are explained in conjunction with Figure 7 which illustrates a schematic showing the interaction of the pattern generation module 218 with other modules of the system 100. The pattern generation module 218 may generate a pattern for rendering on the selected display device. The pattern can be a calibration pattern and may be understood as a preset design or a set of markers that can be scanned by the imaging sensor to determine the properties of the imaging sensor, such as focal length and distortion coefficient. The pattern generation module 218, in one example, may generate a checkered grid or a dotted grid for rendering on the selected display device. In addition, the pattern generation module 218 may change the calibration pattern, if needed, to better perform the calibration process.

[0048] In one example, the generated calibration pattern may be based on an environmental characteristic, a display characteristic of the identified display device, and the calibration error. The environmental characteristics may include lighting conditions and the design of objects in the real world. For instance, the calibration pattern may be a dotted grid in case there is a checkered painting in the real world. Such a selection may be made to prevent the calibration module 210 from accidentally identifying the checkered painting as the calibration pattern. Further, the display characteristics may be the size and type of display. Furthermore, the calibration error may determine the type of calibration pattern. For instance, in case the calibration error is greater, the calibration pattern may be a checkered grid and in case the calibration error is small, the calibration pattern can be a dotted grid.

[0049] The details of the gesture generation module 220 are explained in conjunction with Figure 8 which illustrates a schematic showing the interaction of the gesture generation module 220 with other modules of the system 100. The gesture generation module 220 may be in communication with the calibration module 210 and may be configured to generate trajectory and head gestures that may be rendered to the HMD device 102 as a video overlay. The gesture generation module 220 may, depending on the degree of calibration error, either generate the head gesture or the guidance path or both. In addition, the gesture generation module 220 may relay the information regarding the generation of the gesture / trajectory to the calibration module 210. The gesture generation module 220 may also track the user’s movement in the real-time and generate additional gestures as the user traverses the guidance path. A manner in which the aforementioned modules operate will be explained later.

[0050] Figure 9 illustrates a flow chart of the method 900 for calibrating the HMD device 102, in accordance with an embodiment of the present disclosure. The order in which the method steps are described below is not intended to be construed as a limitation, and any number of the described method steps can be combined in any appropriate order to execute the method or an alternative method. Additionally, individual steps may be deleted from the method without departing from the spirit and scope of the subject matter described herein.

[0051] The method 900 can be performed by programmed computing devices, for example, based on instructions retrieved from non-transitory computer readable media. The computer readable media can include machine-executable or computer-executable instructions to perform all or portions of the described method. The computer readable media may be, for example, digital memories, magnetic storage media, such as magnetic disks and magnetic tapes, hard drives, or optically readable data storage media.

[0052] In one example, the method 900 may be performed partially or completely by the system 100 shown in Figure 2.

[0053] In an embodiment, the method 900, at step 902, the system 100 determines that a calibration error in the HMD device exceeds a predefined threshold value. The system 100 may determine to calibrate the HMD device 102 when the calibration error exceeds the predefined threshold value.

[0054] At step 904, the system 100 identifies at least one display device, to perform the calibration, from a list of display devices available in the real world and within a field of view of the HMD device based on at least one selection parameter. The system 100 may obtain the list of display devices present in vicinity of the HMD device 102 in the real-world, when the calibration error exceeds the predefined threshold value. For collecting the list of display devices available for performing the calibration, the system 100 may identify the display devices that are connected to the same network as the HMD device 102 and registered with the same user account as the HMD device 102. The system 100 may determine at least one display device from the list of display devices based on the at least one selection parameter. The at least one selection parameter may include ambient light conditions around the display device, size of the display device, and vicinity of the HMD device 102 and the display device.

[0055] Further, at step 906, a calibration pattern is displayed on the identified at least one display device. The system 100 may provide the calibration pattern to the identified at least one display device for displaying the calibration pattern.

[0056] At step 908, the system 100 may provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device 102. The user guidance is indicative of movement of a user along a guidance path and guided head gestures to be performed by the user along the guidance path in the real world.

[0057] At step 910, the system 100 may obtain at least one image of the calibration pattern and HMD device motion data along the guidance path and for each of the corresponding guided head gestures performed by the user, respectively.

[0058] Finally, at step 912, the calibration of the HMD device 102 is performed based on at least one of the HMD device motion data and the at least one image. According to an embodiment of the present disclosure, the calibration of the HMD device could be done through the following process. The system 100 may track movement of the user on a portion of the guidance path. The system 100 may provide, as an overlay in the AR video feed, a predefined head gesture prompt upon arrival of the user at a turn in the guidance path. The system 100 may collect the HMD motion data as the user performs head movement based on the predefined head gesture prompt. The system 100 may prompt the user to travel further on the guidance path.

[0059] Figures 10A and 10B illustrate a flow chart of a method 1000 for calibrating the HMD device in real-time, in accordance with an embodiment of the present disclosure. According to an embodiment of the present disclosure, the method 1000 may be performed partially or completely by the system 100 shown in Figure 2. The detail description of any content that is repeated from what was previously explained in Figure 9 is omitted below.

[0060] At step 1002, a calibration error between at least one pair of sensors of the HMD device is tracked in the real time.

[0061] At step 1004, at least one display device present in the vicinity of the HMD device in the real-world is identified, when the tracked calibration error exceeds a pre-defined threshold value.

[0062] At step 1006, at least one identified display device is determined based on display and environmental characteristics associated with each display device.

[0063] At step 1008, the calibration pattern is displayed on the identified display device.

[0064] At step 1010, the user guidance is generated and augmented over a live Augmented Reality (AR) video feed of the real-world in the HMD device. The user guidance includes a guidance path to be followed by the user and at least one head movement to be performed by the user.

[0065] At step 1012, one or more images of at least one of the calibration pattern and HMD device motion data are captured by the HMD device while the user is performing each head movement.

[0066] Finally, at step 1014, at least one calibration parameter is estimated to perform the calibration of the at least one pair of sensors using at least one of the captured images and the HMD device motion data.

[0067] A detailed explanation of the aforementioned methods 900 and 1000 is explained later.

[0068] Figure 11 illustrates schematic 1100 showing the interactions of various module(s) of the system, in accordance with an embodiment of the present disclosure. Upon actuation by the calibration module 210, the imaging sensor of the HMD device 102 may capture a video feed and relay the same to the object detection module 216. Simultaneously, the display selection module 214 may request the display interface module 212 to share the list of display devices. The display interface module 212 may share the list of display devices available for displaying the calibration pattern with the display selection module 214. The display selection module 214 may also receive a selection of the display device from the user.

[0069] Referring now to Figure 12 which illustrates a video feed 1200 presented to the user by the imaging sensor of the HMD 102 showing markers 1202 and 1204, in accordance with an implementation of the present disclosure. As seen in Figure 12, the display interface module 212 may present suggestions in the form of markers 1202 and 1204. Further, the display interface module 212 may provide additional marking ‘recommended’ for the marker 1202 associated with the television (TV). Accordingly, the user may select the television as the recommended display. The display selection module 214 may then receive the calibration pattern from the pattern generation module 218 via the display interface module 212. Thereafter, the display selection module 214 may send the calibration pattern to the display device.

[0070] Referring now to Figure 13 which illustrates another video feed 1300 presented to the user showing a calibration pattern, in accordance with an implementation of the present disclosure. The TV 1302 may render the calibration pattern such that the calibration pattern is within the field of view of the imaging sensor of the HMD 102. As seen clearly, the selected display device, owing to its size, is able to project a large size calibration pattern so that the calibration pattern covers a greater area of the FOV of the imaging sensor of the HMD 102. Further, the imaging sensor of the HMD device 102 may provide a live video feed to a display panel in the HMD device 102 including the calibration pattern 1302. The provided video feed is rendered to the user as an Augmented Reality (AR) video feed.

[0071] Referring to Figures 14A and 14B which illustrates another video feed 1400 showing a user guidance 1402 and user gestures 1404, in accordance with an embodiment of the present disclosure. The gesture generation module 220 may generate a user guidance 1402 based on the objects and the walkable path determined by the object detection module 216. The generated user guidance 1402 may be overlaid on the AR video feed. Further, the user guidance 1402 may include a guidance path and guided head gestures to be performed by the user along the guidance path in the real world. Further, the user may be prompted to look at the calibration pattern displayed on the display device.

[0072] Referring again to Figure 11, the calibration module 210 may then instruct the gesture generation module 220 to generate a gesture, such as a head pose, and upon receiving the instructions, the gesture generation module 220 may send the generated pose as an animation overlaid in the AR video feed. Further, as the user performs the head pose gesture, the calibration module 210 collects the HMD motion data, for instance, from the IMU sensor and a video feed, from the imaging sensor, that includes the tiling of the calibrated pattern displayed on the selected display device. The calibration module 210 may process both the sensor data and the video feed to determine the correction to be made in the video feed to the HMD device 102.

[0073] Although the present illustration refers to head gestures, other types of markers may be provided. In one example, the gesture generation module 220 may actuate the display play to render floating objects such that one of the boxes is stationary and the other box may tilt based on the tilting of the HMD 102. Further, the user may be prompted to tilt the HMD 102 so that both the boxes superimpose each other thereby completing the calibration step. In another example, the gesture generation module 220 may actuate the display play to render a plurality of floating stationary objects and a floating cursor, such that the floating cursor moves in the video feed based on the head movement. Further, the user may be instructed by the calibration module 210 to tilt its head in different directions in a preset manner so that the floating cursor superimposes in a preset manner. In yet another example, the gesture generation module 220 may actuate the display device to render a floating stationary path and a cursor such that the floating cursor moves in the video feed based on the head movement. Further, the user may be instructed to move the head so that the cursor describes the floating path. Furthermore, the calibration module 210 may track the head movement and prompt the user if the cursor deviates from the floating path.

[0074] According to an embodiment of the present disclosure, the calibration module 210 may determine that the user has tilted the HMD device 102 by 10 degrees. The calibration module 210 may then process the video feed using a known image processing technique to determine the rotation of the calibration pattern in the video feed. For instance, the calibration module 210 determines that the degree of rotation of the calibrated pattern is about 12 degrees. Accordingly, the calibration module 210 may apply keystone correction in the video feed to rectify the calibration error. Thereafter, the calibration module 210 may send another instruction to the gesture generation module 220 to prompt the user to re-perform the head pose. The calibration module 210 may again receive and process the sensor data and the video feed to check if the calibration error is rectified.

[0075] According to an embodiment of the present disclosure,, in the case of multiple imaging sensors, the calibration module 210 may receive and process individual video feed. As a part of the processing, the calibration module 210 may determine a degree of rotation in the calibration pattern in each video feed. Thereafter, the calibration module 210 may identify the video feed in which the degree of rotation mismatches with the correct degree of rotation. Accordingly, the calibration module 210 may apply keystone correction on the video feed with the mismatched degree of rotation.

[0076] In case the correction in the calibration may not be enough, the calibration module 210 may instruct the gesture generation module 220 to prompt the user to traverse the guidance path. As the user travels on the guidance path, the gesture generation module 220 may track the movement of the user on a portion of the guidance path using the image processing technique to determine the arrival of the user at a turn of the guidance path. Upon the arrival of the user at the guidance path, the gesture generation module 220 may prompt the user to perform another predefined head gesture 1406 as shown in Figure 14B. As the user performs the predefined head gesture at the turn, the calibration module 210 may collect the HMD motion data along with the video feed of the calibration pattern recorded at the turn. The calibration module 210 may again compute the degree of rotation in the manner explained above. Thereafter, the gesture generation module 220 may prompt the user to travel further along the guidance path. Further, the gesture generation module 220 and the calibration module 210 may perform the capturing and determination of the degree of rotation on all subsequent turns on the guidance path. The calibration module 210 may perform the aforementioned task until the calibration error is rectified. Further, another overlay 1408 may be presented to the user indicating the degree of both the IMU sensor and the imaging sensors. In the illustrated example, the calibration module 210 may render an overlay indicating that the calibration of the IMU sensor is at 100% whereas the calibration of the imaging sensor is at 80%.

[0077] According to an embodiment of the present disclosure, there may be a scenario where the user may be in front of a mirror, such that the mirror may project a reflection of the calibrated pattern displayed in the selected display device. In such a scenario, the calibration module 210 may also consider the environmental characteristic, the display characteristic, and the calibration error while performing the calibration. Such consideration is needed to compensate for the error that may be introduced by the mirror. The compensation may include but is not limited to, adjusting the guidance path prior to the rectification of calibration error. Such a correction is needed because the guidance path overlaid based on the mirror may be laterally inverted.

[0078] According to an embodiment of the present disclosure, the calibration module 210 may instruct the pattern generation module 218 to generate the calibration pattern in accordance with the environmental characteristic, the display characteristic, and the calibration error. Once the calibration pattern is displayed, the calibration module 210 may again collect the video feed and the HMD motion data and determine the calibration error.

[0079] According to an embodiment of the present disclosure, The gesture generation module 220 may then provide a prompt to the user to travel across the guidance path. As the user travels on the guidance path, the calibration module 210 may adjust the display parameters, such as brightness and / or contrast level by sending instructions to the display device. In addition, the calibration module 210 may instruct the display selection module 214 to adjust the design of the displayed calibration pattern. The calibration module 210 may simultaneously track the movement of the user in the real world. Based on the tracked movement, the calibration module 210 may provide AR feedback in the video feed. The AR feedback may include an adjustment in the guidance path which may include a lateral inversion of the coordinates of the guidance path so that the guidance path coincides with a real walkable path in the real world.

[0080] According to an embodiment of the present disclosure, the display selection module 214 may display the calibration pattern on the identified at least one display device when the HMD device is ready for calibration and is in front of the mirror. Further, the display selection module 214 may temporally change a size of the calibration pattern. Simultaneously, the calibration module 210 may observe the calibration pattern that is reflected by the mirror in the video feed generated by one or more cameras in the HMD device 102. The calibration module 210 may also compute a distance between the HMD device 102 and the mirror by correlating a calculation data generated by the plurality of cameras with saved distance calculation data. In addition, the calibration module 210 may perform multiple calibrations. For instance, multiple calibrations may include IMU calibration to the plurality of cameras, intrinsic calibration of individual cameras, and extrinsic calibration with respect to the rest of the plurality of cameras. Based on the calibration, the calibration module 210 may generate a feedback about the quality of calibration and the regions that were not observed properly by the plurality of cameras. Based on the feedback, the calibration module may change one of the display properties or the calibration pattern in the display screen based on the feedback. Once the changes are made, the calibration module 210 may perform subsequent IMU calibration and adjustments based on the feedback.

[0081] Accordingly, the system 100 of the present disclosure enables calibration to correct large calibration errors. Moreover, the system 100 may provide options to perform different levels of calibration. Moreover, since the calibration error is monitored in the real time, the system 100 may initiate the calibration as soon as the calibration error exceeds the threshold value. Such an approach ensures that the user’s experience is not hindered. Moreover, since the system 100 is capable of rectifying calibration errors that are not easily perceptible by the user, the system 100 may prevent discomfort to the user that would otherwise occur in long-term usage of the HMD device 102 with the calibration error.

[0082] According to an embodiment of the disclosure, a method for calibrating a head mounted display (HMD) device 102 is provided. The method may include determining 902 that a calibration error in the HMD device 102 exceeds a predefined threshold value. The method may include identifying 904, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device 102 based on at least one selection parameter. The method may include displaying 906 a calibration pattern on the identified at least one display device. The method may include providing 908 a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device 102, wherein the user guidance is indicative of movement of a user along a guidance path and guided head gestures to be performed by the user along the guidance path in the real world. The method may include obtaining 910 at least one image of the calibration pattern and HMD motion data along the guidance path and for each of the corresponding guided head gestures performed by the user, respectively. The method may include performing 912 the calibration of the HMD device 102 based on at least one of the HMD motion data and the at least one image.

[0083] According to an embodiment of the disclosure, the identifying of the at least one display device includes obtaining the list of display devices present in vicinity of the HMD device 102 in the real-world, when the calibration error exceeds the predefined threshold value. The identifying of the at least one display device includes determining at least one display device from the list of display devices based on the at least one selection parameter.

[0084] According to an embodiment of the disclosure, the obtaining of the list of display devices comprises identifying the display devices that are connected to the same network as the HMD device 102 and registered with the same user account as the HMD device 102.

[0085] According to an embodiment of the disclosure, the at least one selection parameter may include ambient light conditions around the display device, size of the display device, and vicinity of the HMD device 102 and the display device.

[0086] According to an embodiment of the disclosure, the method may further include obtaining a plurality of images from a plurality of cameras of the HMD device 102 to identify a plurality of objects around the identified at least one display device. The method may further include identifying a walkable path between the user and the identified at least one display device based on the plurality of identified objects to provide the overlaid guidance path in the AR video feed.

[0087] According to an embodiment of the disclosure, the performing of the calibration may include tracking movement of the user on a portion of the guidance path. The performing of the calibration may include providing, as an overlay in the AR video feed, a predefined head gesture prompt upon arrival of the user at a turn in the guidance path. The performing of the calibration may include collecting the HMD motion data as the user performs head movement based on the predefined head gesture prompt. The performing of the calibration may include prompting the user to travel further on the guidance path.

[0088] According to an embodiment of the disclosure, a system 100 for calibrating a Head Mounted Display (HMD) device 102 is provided. The system 100 may include a memory 204 storing instructions and at least one processor 202 in communication with the memory 204. The instructions, when executed by the at least one processor 202, may cause the system 100 to determine that a calibration error in the HMD device 102 exceeds a predefined threshold value. The instructions, when executed by the at least one processor 202, may cause the system 100 to identify, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device 102 based on at least one selection parameter. The instructions, when executed by the at least one processor 202, may cause the system 100 to display a calibration pattern on the identified at least one display device. The instructions, when executed by the at least one processor 202, may cause the system 100 to provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device 102, wherein the user guidance is indicative of a movement of a user along a guidance path and head gestures in the real world to perform the calibration. The instructions, when executed by the at least one processor 202, may cause the system 100 to obtain at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and corresponding head gestures, respectively. The instructions, when executed by the at least one processor 202, may cause the system 100 to perform the calibration of the HMD device 102 based on at least one of the HMD motion data and the at least one image.

[0089] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor 202, may cause the system to obtain the list of display devices present in vicinity of the HMD device 102 in the real-world, when the calibration error exceeds the predefined threshold value. The instructions, when executed by the at least one processor 202, may cause the system to determine at least one display device from the list of display devices based on the at least one selection parameter.

[0090] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor 202, may cause the system to identify the display devices that are connected to the same network as the HMD device 102 and registered with the same user account as the HMD device 102.

[0091] According to an embodiment of the disclosure, the at least one selection parameter may include ambient light conditions around the display device, size of the display device, and vicinity of the HMD device 102 and the display device.

[0092] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor 202, may cause the system to obtain a plurality of images from a plurality of cameras of the HMD device 102 to identify a plurality of objects around the identified at least one display device. The instructions, when executed by the at least one processor 202, may cause the system to identifying a walkable path between the user and the identified at least one display device based on the plurality of identified objects to provide the overlaid guidance path in the AR video feed.

[0093] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor 202, may cause the system to track movement of the user on a portion of the guidance path. The instructions, when executed by the at least one processor 202, may cause provide, as an overlay in the AR video feed, a predefined head gesture prompt upon arrival of the user at a turn in the guidance path. The system to the instructions, when executed by the at least one processor 202, may cause the system to collect the HMD motion data as the user performs head movement based on the predefined head gesture prompt. The instructions, when executed by the at least one processor 202, may cause the system to prompt the user to travel further on the guidance path.

[0094] According to an embodiment of the disclosure, a computer-readable storage medium storing instructions is provided. The instructions, when executed by the at least one processor, may cause the at least one processor to determine that a calibration error in the HMD device 102 exceeds a predefined threshold value. The instructions, when executed by the at least one processor, may cause the at least one processor to identify, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device 102 based on at least one selection parameter. The instructions, when executed by the at least one processor, may cause the at least one processor to display a calibration pattern on the identified at least one display device. The instructions, when executed by the at least one processor, may cause the at least one processor to provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device 102, wherein the user guidance is indicative of a movement of a user along a guidance path and head gestures in the real world to perform the calibration. The instructions, when executed by the at least one processor, may cause the at least one processor to obtaining at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and corresponding head gestures, respectively. The instructions, when executed by the at least one processor, may cause the at least one processor to perform the calibration of the HMD device 102 based on at least one of the HMD motion data and the at least one image.

[0095] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor, may cause the at least one processor to obtain the list of display devices present in vicinity of the HMD device 102 in the real-world, when the calibration error exceeds the predefined threshold value. The instructions, when executed by the at least one processor, may cause the at least one processor to determine at least one display device from the list of display devices based on the at least one selection parameter.

[0096] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor, may cause the at least one processor to identify the display devices that are connected to the same network as the HMD device 102 and registered with the same user account as the HMD device 102.

[0097] According to an embodiment of the disclosure, the at least one selection parameter may include ambient light conditions around the display device, size of the display device, and vicinity of the HMD device 102 and the display device.

[0098] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor, may cause the at least one processor to obtain a plurality of images from a plurality of cameras of the HMD device 102 to identify a plurality of objects around the identified at least one display device. The instructions, when executed by the at least one processor, may cause the at least one processor to identifying a walkable path between the user and the identified at least one display device based on the plurality of identified objects to provide the overlaid guidance path in the AR video feed.

[0099] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor, may cause the at least one processor to track movement of the user on a portion of the guidance path. The instructions, when executed by the at least one processor, may cause the at least one processor to provide, as an overlay in the AR video feed, a predefined head gesture prompt upon arrival of the user at a turn in the guidance path. The instructions, when executed by the at least one processor, may cause the at least one processor to collect the HMD motion data as the user performs head movement based on the predefined head gesture prompt. The instructions, when executed by the at least one processor, may cause the at least one processor to prompt the user to travel further on the guidance path.

[0100] According to an embodiment of the disclosure, a method for calibrating a head mounted display (HMD) device 102 is provided. The method may include tracking, in real-time, a calibration error between at least one pair of sensors of the HMD device 102. The method may include identifying at least one display device present in vicinity of the HMD device 102 in the real-world, when the tracked calibration error exceeds a pre-defined threshold value. The method may include determining at least one identified display device based on display and environmental characteristics associated with each display device. The method may include displaying a calibration pattern on the identified display device. The method may include generating a user guidance augmented over a live Augmented Reality (AR) video feed of the real-world in the HMD device 102, wherein the user guidance includes a guidance path to be followed by the user and at least one head movement to be performed by the user. The method may include capturing by the HMD device 102, one or more images of at least one of the calibration pattern and HMD motion data while the user is performing each head movement. The method may include estimating at least one calibration parameter to perform the calibration of the at least one pair of sensors using at least one of the captured images and the HMD motion data.

[0101] According to an embodiment of the disclosure, the method may include generating the calibration pattern in accordance with an environmental characteristic, a display characteristic of the identified display device, and the calibration error. The method may include adjusting one of the display parameters and a design of the calibration pattern based on the characteristics of the environment in the real world and simultaneously tracking movement of the user in the real world. The method may include providing an Augmented Reality (AR) feedback to the user via the AR video feed and adjusting the guidance path in the real time based on the AR feedback.

[0102] According to an embodiment of the disclosure, the method may include displaying the calibration pattern on the identified at least one display device when the HMD device 102 is ready for calibration and is in front of a mirror. The method may include changing the calibration pattern size temporally and observing the calibration pattern that is reflected by the mirror with a plurality of cameras in the HMD device 102. The method may include computing the distance between the HMD device 102 and the mirror by correlating a calculation data generated by the plurality of cameras with saved distance calculation data. The method may include performing an Inertial Measurement Unit (IMU) calibration to the plurality of cameras, intrinsic calibration of individual camera, and extrinsic calibration with respect to the rest of the plurality of cameras. The method may include generating feedback about the quality of calibration and the regions that were not observed properly by the plurality of cameras. The method may include changing one of display properties or the calibration pattern in the display screen based on the feedback. The method may include performing subsequent IMU calibration and adjustments based on the feedback

[0103] According to an embodiment of the disclosure, a system 100 for calibrating a Head Mounted Display (HMD) device 102 is provided. The system 100 may include a memory 204 storing instructions and at least one processor 202 in communication with the memory 204. The instructions, when executed by the at least one processor 202, may cause the system 100 to track, in real-time, a calibration error between at least one pair of sensors of the HMD device 102. The instructions, when executed by the at least one processor 202, may cause the system 100 to identify at least one display device present in vicinity of the HMD device 102 in the real-world, when the tracked calibration error exceeds a pre-defined threshold value. The instructions, when executed by the at least one processor 202, may cause the system 100 to determine at least one identified display device based on display and environmental characteristics associated with each display device. The instructions, when executed by the at least one processor 202, may cause the system 100 to display a calibration pattern on the identified display device. The instructions, when executed by the at least one processor 202, may cause the system 100 to generate a user guidance augmented over a live Augmented Reality (AR) video feed of the real-world in the HMD device 102, wherein the user guidance includes a guidance path to be followed by the user and at least one head movement to be performed by the user. The instructions, when executed by the at least one processor 202, may cause the system 100 to capture by the HMD device 102, one or more images of at least one of the calibration pattern and HMD motion data while the user is performing each head movement. The instructions, when executed by the at least one processor 202, may cause the system 100 to estimate at least one calibration parameter to perform the calibration of the at least one pair of sensors using at least one of the captured images and the HMD motion data.

[0104] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor 202, may cause the system to generate the calibration pattern in accordance with an environmental characteristic, a display characteristic of the identified display device, and the calibration error. The instructions, when executed by the at least one processor 202, may cause the system to adjust one of the display parameters and a design of the calibration pattern based on the characteristics of the environment in the real world and simultaneously track movement of the user in the real world. The instructions, when executed by the at least one processor 202, may cause the system to provide an Augmented Reality (AR) feedback to the user via the AR video feed and adjusting the guidance path in the real time based on the AR feedback.

[0105] According to an embodiment of the disclosure, the instructions, when executed by the at least one processor 202, may cause the system to display the calibration pattern on the identified at least one display device when the HMD device 102 is ready for calibration and is in front of a mirror. The instructions, when executed by the at least one processor 202, may cause the system to change the calibration pattern size temporally and observe the calibration pattern that is reflected by the mirror with a plurality of cameras in the HMD device 102. The instructions, when executed by the at least one processor 202, may cause the system to compute the distance between the HMD device 102 and the mirror by correlating a calculation data generated by the plurality of cameras with saved distance calculation data. The instructions, when executed by the at least one processor 202, may cause the system to perform an Inertial Measurement Unit (IMU) calibration to the plurality of cameras, intrinsic calibration of individual camera, and extrinsic calibration with respect to the rest of the plurality of cameras. The instructions, when executed by the at least one processor 202, may cause the system to generate feedback about the quality of calibration and the regions that were not observed properly by the plurality of cameras. The instructions, when executed by the at least one processor 202, may cause the system to change one of display properties or the calibration pattern in the display screen based on the feedback. The instructions, when executed by the at least one processor 202, may cause the system to perform subsequent IMU calibration and adjustments based on the feedback.

[0106] According to an embodiment of the disclosure, a computer-readable storage medium storing instructions is provided. The instructions, when executed by the at least one processor, may cause the at least one processor to track, in real-time, a calibration error between at least one pair of sensors of the HMD device 102. The instructions, when executed by the at least one processor, may cause the at least one processor to identify at least one display device present in vicinity of the HMD device 102 in the real-world, when the tracked calibration error exceeds a pre-defined threshold value. The instructions, when executed by the at least one processor, may cause the at least one processor to determine at least one identified display device based on display and environmental characteristics associated with each display device. The instructions, when executed by the at least one processor, may cause the at least one processor to display a calibration pattern on the identified display device. The instructions, when executed by the at least one processor, may cause the at least one processor to generate a user guidance augmented over a live Augmented Reality (AR) video feed of the real-world in the HMD device 102, wherein the user guidance includes a guidance path to be followed by the user and at least one head movement to be performed by the user. The instructions, when executed by the at least one processor, may cause the at least one processor to capture by the HMD device 102, one or more images of at least one of the calibration pattern and HMD motion data while the user is performing each head movement. The instructions, when executed by the at least one processor, may cause the at least one processor to estimate at least one calibration parameter to perform the calibration of the at least one pair of sensors using at least one of the captured images and the HMD motion data.

[0107] In this application, unless specifically stated otherwise, the use of the singular includes the plural and the use of “or” means “and / or.” Furthermore, the use of the terms “including” or “having” is not limiting. Any range described herein will be understood to include the endpoints and all values between the endpoints. Features of the disclosed embodiments may be combined, rearranged, omitted, etc., within the scope of the present disclosure to produce additional embodiments. Furthermore, certain features may sometimes be used to advantage without a corresponding use of other features.

[0108] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist.

Claims

1.A method (900) for calibrating a head mounted display (HMD) device (102), comprising:determining (902) that a calibration error in the HMD device (102) exceeds a predefined threshold value;identifying (904), for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device (102) based on at least one selection parameter;providing (906) a calibration pattern to the identified at least one display device for displaying the calibration pattern;providing (908) a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device (102), wherein the user guidance is indicative of movement of a user along a guidance path and guided head gestures to be performed by the user along the guidance path in the real world;obtaining (910) at least one image of the calibration pattern and HMD motion data along the guidance path and for each of the corresponding guided head gestures performed by the user, respectively; andperforming (912) the calibration of the HMD device (102) based on at least one of the HMD motion data and the at least one image.2.The method (900) as claimed in any one of claims 1, wherein the identifying of the at least one display device comprises:obtaining the list of display devices present in vicinity of the HMD device (102) in the real-world, when the calibration error exceeds the predefined threshold value; anddetermining at least one display device from the list of display devices based on the at least one selection parameter.3.The method (900) as claimed in claim 1, wherein the obtaining of the list of display devices comprises identifying the display devices that are connected to the same network as the HMD device (102) and registered with the same user account as the HMD device (102).4.The method (900) as claimed in any one of claims 1 to 3, wherein the at least one selection parameter includes ambient light conditions around the display device, size of the display device, and vicinity of the HMD device (102) and the display device.5.The method (900) as claimed in any one of claims 1 to 4, further comprising:obtaining a plurality of images from a plurality of cameras of the HMD device (102) to identify a plurality of objects around the identified at least one display device; andidentifying a walkable path between the user and the identified at least one display device based on the plurality of identified objects to provide the overlaid guidance path in the AR video feed.6.The method (900) as claimed in any one of claims 1 to 5, wherein the performing of the calibration comprises:tracking movement of the user on a portion of the guidance path;providing, as an overlay in the AR video feed, a predefined head gesture prompt upon arrival of the user at a turn in the guidance path;collecting the HMD motion data as the user performs head movement based on the predefined head gesture prompt; andprompting the user to travel further on the guidance path.7.A system (100) to calibrate a Head Mounted Display (HMD) device (102), the system (100) comprising:a memory (204) storing instructions; andat least one processor (202) in communication with the memory (204),wherein the instructions, when executed by the at least one processor (202), cause the system to:determine that a calibration error in the HMD device (102) exceeds a predefined threshold value;identify, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device (102) based on at least one selection parameter;provide a calibration pattern to the identified at least one display device for displaying the calibration pattern;provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device (102), wherein the user guidance is indicative of a movement of a user along a guidance path and head gestures in the real world to perform the calibration;obtain at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and corresponding head gestures, respectively; andperform the calibration of the HMD device (102) based on at least one of the HMD motion data and the at least one image.8.The system (100) as claimed in claim 7, wherein the instructions, when executed by the at least one processor (202), cause the system to:obtain the list of display devices present in vicinity of the HMD device (102) in the real-world, when the calibration error exceeds the predefined threshold value; anddetermine at least one display device from the list of display devices based on the at least one selection parameter.9.The system (100) as claimed in claim 7 or claim 8, wherein the instructions, when executed by the at least one processor (202), cause the system to identify the display devices that are connected to the same network as the HMD device (102) and registered with the same user account as the HMD device (102).10.The system (100) as claimed in any one of claims 7 to 9, wherein the at least one selection parameter includes ambient light conditions around the display device, size of the display device, and vicinity of the HMD device (102) and the display device.11.The system (100) as claimed in any one of claims 7 to 10, wherein the instructions, when executed by the at least one processor (202), cause the system to:obtain a plurality of images from a plurality of cameras of the HMD device (102) to identify a plurality of objects around the identified at least one display device; andidentifying a walkable path between the user and the identified at least one display device based on the plurality of identified objects to provide the overlaid guidance path in the AR video feed.12.The system (100) as claimed in any one of claims 7 to 11, wherein the instructions, when executed by the at least one processor (202), cause the system to:track movement of the user on a portion of the guidance path;provide, as an overlay in the AR video feed, a predefined head gesture prompt upon arrival of the user at a turn in the guidance path;collect the HMD motion data as the user performs head movement based on the predefined head gesture prompt; andprompt the user to travel further on the guidance path.13.A computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to:determine that a calibration error in the HMD device (102) exceeds a predefined threshold value;identify, for performing calibration, at least one display device from a list of display devices available in the real world and within a field of view of the HMD device (102) based on at least one selection parameter;provide a calibration pattern to the identified at least one display device for displaying the calibration pattern;provide a user guidance overlaid on an augmented reality (AR) video feed of the real-world in the HMD device (102), wherein the user guidance is indicative of a movement of a user along a guidance path and head gestures in the real world to perform the calibration;obtain at least one image of the calibration pattern and HMD motion data at a plurality of positions of the user along the guidance path and corresponding head gestures, respectively; andperform the calibration of the HMD device (102) based on at least one of the HMD motion data and the at least one image.14.The computer-readable storage medium as claimed in claim 13, wherein the instructions, when executed by the at least one processor, cause the at least one processor to:obtain the list of display devices present in vicinity of the HMD device (102) in the real-world, when the calibration error exceeds the predefined threshold value; anddetermine at least one display device from the list of display devices based on the at least one selection parameter.15.The computer-readable storage medium as claimed in claim 13 or claim 14, wherein the instructions, when executed by the at least one processor, cause the at least one processor to:obtain a plurality of images from a plurality of cameras of the HMD device (102) to identify a plurality of objects around the identified at least one display device; andidentifying a walkable path between the user and the identified at least one display device based on the plurality of identified objects to provide the overlaid guidance path in the AR video feed.

Images