WO2017092339A1 - 一种收集传感器数据的处理方法和装置 - Google Patents

一种收集传感器数据的处理方法和装置 Download PDF

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Publication number
WO2017092339A1
WO2017092339A1 PCT/CN2016/089334 CN2016089334W WO2017092339A1 WO 2017092339 A1 WO2017092339 A1 WO 2017092339A1 CN 2016089334 W CN2016089334 W CN 2016089334W WO 2017092339 A1 WO2017092339 A1 WO 2017092339A1
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Prior art keywords
sensor data
target
data
target sensor
deviation
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English (en)
French (fr)
Inventor
胡雪莲
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Le Holdings Beijing Co Ltd
Leshi Zhixin Electronic Technology Tianjin Co Ltd
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Le Holdings Beijing Co Ltd
Leshi Zhixin Electronic Technology Tianjin Co Ltd
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Priority to US15/246,408 priority Critical patent/US20170161953A1/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/131—Protocols for games, networked simulations or virtual reality
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B27/00—Optical systems or apparatus not provided for by any of the groups G02B1/00 - G02B26/00, G02B30/00
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/012—Head tracking input arrangements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/14—Digital output to display device ; Cooperation and interconnection of the display device with other functional units
    • G06F3/147—Digital output to display device ; Cooperation and interconnection of the display device with other functional units using display panels
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating three-dimensional [3D] models or images for computer graphics
    • G06T19/006—Mixed reality
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/20—Scenes; Scene-specific elements in augmented reality scenes
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G5/00—Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators

Definitions

  • the present invention relates to the field of virtual reality technologies, and in particular, to a processing method for collecting sensor data and a processing device for collecting sensor data.
  • Virtual Reality also known as Spiritual Reality or Virtual Reality
  • VR is a multi-dimensional sensory environment that is generated in whole or in part by computer, such as vision, hearing, and touch.
  • auxiliary sensing device such as helmet display and data glove, it provides a multi-dimensional human-machine interface for observing and interacting with the virtual environment, so that people can enter the virtual environment and directly observe the internal changes of things and interact with things. , giving people a sense of "immersive".
  • sensor data is the source of the video frame.
  • the sensor data refers to the data collected by the sensor installed on the sensing device.
  • the inventor found that whether the sensor data is accurate or not is directly related to calculating the field of view angle, rendering the scene and video according to the angle of view, and the time delay effect (TimeWarp) processing.
  • the image generated by the mobile phone-based VR system may drift autonomously, that is, the user may see the self-drifting image, which reduces the user experience; and may cause problems in the operation focus.
  • the technical problem to be solved by the embodiments of the present invention is to provide a processing method for collecting sensor data.
  • the method solves the problem that the image autonomously shifts due to abnormal sensor data and that it is difficult to point when operating the focus.
  • the embodiment of the invention further provides a processing device for collecting sensor data, which is used to ensure the implementation and application of the above method.
  • the embodiment of the present invention discloses a processing method for collecting sensor data, including:
  • the target sensor data is compared with the virtual sensor data, and the abnormal target sensor data is excluded according to the comparison result.
  • the embodiment of the invention further discloses a processing device for collecting sensor data, comprising:
  • a fitting curve generating module for generating a fitting curve based on historical sensor data
  • a target time determining module configured to determine a target time corresponding to the target sensor data when the target sensor data is detected
  • a virtual data calculation module configured to calculate the target time according to the fitting curve, to obtain virtual sensor data corresponding to the target time
  • the data comparison module is configured to compare the target sensor data with the virtual sensor data, and exclude abnormal target sensor data according to the comparison result.
  • An embodiment of the present invention provides a computer program comprising computer readable code, when the computer readable code is run on a mobile terminal, causing the mobile terminal to perform the above-described processing method of collecting sensor data.
  • Embodiments of the present invention provide a computer readable medium in which the above computer program is stored.
  • the embodiments of the invention include the following advantages:
  • the embodiment determines the target time corresponding to the target sensor data, and calculates the target time according to the fitting curve to obtain the virtual sensor data corresponding to the target time; by comparing the target sensor data with the virtual sensor Data, according to the comparison result, the abnormal target sensor data is excluded, that is, by detecting the degree of coincidence between the target sensor data and the fitting curve, the abnormal target sensor data is eliminated, thereby solving the problem that the sensor data is abnormal and the image autonomously shifts and the operation focus is difficult.
  • the problem in is .
  • FIG. 1 is a flow chart showing the steps of an embodiment of a method for processing sensor data collected by the present invention
  • FIG. 2 is a flow chart showing the steps of a preferred embodiment of a method for processing sensor data collected by the present invention
  • 3A is a structural block diagram of an embodiment of a processing apparatus for collecting sensor data according to the present invention.
  • 3B is a block diagram showing the structure of a preferred embodiment of a processing apparatus for collecting sensor data according to the present invention.
  • Figure 4 shows schematically a block diagram of a mobile terminal for carrying out the method according to the invention
  • Fig. 5 schematically shows a storage unit for holding or carrying program code implementing the method according to the invention.
  • head tracking is used to change the angle of view of the image, so that the user's visual system and the motion sensing system can be connected and feel more realistic.
  • the position of the user's head can be tracked by the position tracker to determine the motion state of the user's head.
  • the position tracker refers to a device that acts on space tracking and positioning, and is generally used in combination with other VR devices, such as data helmets, stereo glasses, data gloves, etc., so that participants can freely move and rotate in space, without limitation.
  • sensor data is the source of the video frame.
  • Whether the sensor data is accurate or not is directly related to calculating the angle of view, rendering scenes and videos according to the angle of view, and subsequent steps such as TimeWarp processing.
  • the sensor data When the sensor data is abnormal, it will cause the image to autonomously shift, and the problem that is difficult to point when operating the focus.
  • One of the core concepts of the embodiments of the present invention is to generate a fitting curve based on historical sensor data, and to detect abnormality of the target sensor data by detecting the coincidence degree of the target sensor data and the fitting curve, thereby solving the abnormality of the sensor data.
  • a problem that is difficult to achieve when the image is autonomously offset and the focus is manipulated.
  • FIG. 1 a flow chart of steps of a method for processing sensor data collection according to the present invention is shown, which may specifically include the following steps:
  • Step 101 generating a fitting curve based on historical sensor data.
  • the mobile terminal-based VR system can monitor the movement state of the user's head through auxiliary sensing devices such as data helmets, stereo glasses, and data gloves.
  • the VR system based on the mobile terminal can determine the current rotation state of the user by monitoring and collecting the data collected by the sensor.
  • the VR system can use the data collected by the sensor as sensor data, calculate the collected sensor data, determine the angle of view, and render the image frame in real time according to the determined field of view angle, and generate a stereoscopic image corresponding to each scene.
  • the mobile terminal refers to a computer device that can be used in the mobile, such as a smart phone, a notebook computer, a tablet computer, etc., which is not limited in this embodiment of the present invention.
  • the embodiment of the present invention will be described in detail by taking a mobile phone as an example.
  • the collected sensor data can be used as historical sensor data to form a sequence of sensor data, and the sequence is simulated to generate a fitting curve corresponding to the sensor data.
  • the above step 101 may include the following sub-steps:
  • Sub-step 1010 collecting sensor data collected by the sensor.
  • Sub-step 1012 the collected sensor data is used as historical sensor data to form a sequence.
  • Sub-step 1013 performing simulation calculation on the historical sensor data of the sequence to generate a fitting curve.
  • Step 103 When the target sensor data is detected, determine a target time corresponding to the target sensor data.
  • the detected new sensor data may be used as the target sensor data; and the current time may be acquired, and the acquired current time is used as the time detected by the target sensor data, and the target sensor is determined as the target sensor.
  • the target time corresponding to the data For example, the mobile phone-based VR system detects the new sensor data D0, that is, the target sensor data is D0, acquires the current system time as T1, and determines the current system time T1 as the target time corresponding to the target sensor data D0.
  • Step 105 Calculate the target time according to the fitting curve, and obtain virtual sensor data corresponding to the target time.
  • the mobile phone-based VR system can calculate the target time by using a fitting curve to obtain ideal sensor data corresponding to the target time, and the ideal sensor data can also be referred to as virtual sensor data.
  • Step 107 Compare the target sensor data with the virtual sensor data, and exclude abnormal target sensor data according to the comparison result.
  • the mobile phone based VR system can preset the deviation threshold of the sensor data.
  • the deviation threshold is used to determine whether the detected sensor data is abnormal data, that is, when the deviation of the target sensor data from the corresponding virtual sensor data is greater than the deviation threshold, the target sensor data may be determined as abnormal data; when the target sensor data When the deviation of the corresponding virtual sensor data is not greater than the deviation threshold, the target sensor data may be determined as normal data. Specifically, by calculating a difference between the target sensor data and the virtual sensor data, determining whether the difference is greater than a preset deviation threshold, and discarding the target sensor data when the difference is greater than a preset deviation threshold may be excluded. Or delete abnormal target sensor data.
  • the target sensor data D0 and the virtual sensor data D1 is
  • is compared with the preset deviation threshold Y, when the difference is When
  • step 107 may comprise the following sub-steps:
  • Sub-step 1070 calculating a difference between the target sensor data and the virtual sensor data, and determining the deviation data
  • Sub-step 1072 determining whether the deviation data is greater than a preset deviation threshold
  • the target sensor data is used as abnormal target sensor data, and the abnormal target sensor data is deleted.
  • the VR system based on the mobile terminal determines the target time corresponding to the target sensor data, and calculates the target time according to the fitting curve, thereby obtaining the virtual sensor data corresponding to the target time; and the target sensor data and the virtual The sensor data is compared, and the abnormal target sensor data is excluded according to the comparison result, that is, by detecting the coincidence degree of the target sensor data and the fitting curve, the abnormal target sensor data is eliminated, thereby solving the abnormality of the sensor data and causing the image autonomous shift and the operation focus. It is difficult to point to the problem.
  • FIG. 2 a flow chart of the steps of the method for processing the collected sensor data of the present invention is shown, which may specifically include the following steps:
  • step 201 the sensor data collected by the sensor is collected.
  • the mobile phone-based VR system monitors the user's status in real time and renders the video or scene according to the user's status to enhance the user experience.
  • the uploading speed of the sensor data D is fast, that is, the sensing device can upload a plurality of sensor data D to the VR system within a delay time of one data frame.
  • the sensor data D may include, but is not limited to, gyroscope data (such as head orientation) and accelerometer data (such as the magnitude and direction of acceleration received by the mobile phone), and the like. This is not a limitation.
  • the embodiment of the present invention will be described by taking the sensor data as a type of sensor data as an example, but it should not be construed as limiting the embodiment of the present invention.
  • the mobile phone-based VR system can perform data statistics and save, that is, collect the sensor data D collected by the sensor. Specifically, in order to improve the accuracy of the collected sensor data, the VR system can monitor the transmission uploaded by the sensor. For sensor data, the sensor data is calculated for every X uploads, and the average value of each X sensor data is obtained, and the average value is saved. Where X is an integer, such as 1, 2, 3, and the like. For example, the mobile phone-based VR system calculates the sensor data D of each type, and calculates the average of the same type of sensor data D every 3 times by calculating the sensor data D every 3 times. Save it.
  • step 203 the collected sensor data is used as historical sensor data to form a sequence.
  • the VR system can calculate the average value as the historical sensor data Dtn, and sequentially store it in the corresponding sequence to form the sequence LD of the sensor data D.
  • tn represents the time when the average value Dtn of the sensor data is generated, which is equivalent to the time when the VR system collects the historical sensor data Dtn
  • n is an integer such as 1, 2, 3, 4, and the like.
  • the mobile phone-based VR system will refer to the average value Dtn of the sensor data D at different times tn (eg, t1, t2, t3, ...) (eg, Dt1, Dt2, Dt3].
  • the sequence LD of the sensor data D is formed in the corresponding state sequence LD.
  • the sequence LD is set to a sequence in which 30 historical sensor data can be stored, that is, the latest collected 30 historical sensor data Dtn can be stored.
  • the sequence LD of the sensor data D is entered.
  • Step 205 Perform simulation calculation on the historical sensor data of the sequence to generate a fitting curve.
  • D represents sensor data
  • t represents the time at which sensor data D is generated, which corresponds to the time corresponding to the sensor data.
  • Step 207 When the target sensor data is detected, determine a target time corresponding to the target sensor data.
  • the mobile phone-based VR system uses the latest sensor data as the target sensor data D0 when the latest sensor data collected by the sensor is detected, and determines the target time T1 corresponding to the target sensor data D0.
  • Step 209 Calculate the target time according to the fitting curve, and obtain virtual sensor data corresponding to the target time.
  • Step 211 calculating a difference between the target sensor data and the virtual sensor data, and determining the deviation data.
  • the mobile phone-based VR system calculates the target sensor data D0 and the virtual sensor data D1, and obtains a difference
  • Step 213 Determine whether the deviation data is greater than a preset deviation threshold.
  • the mobile phone-based VR system can determine whether the deviation data corresponding to the target sensor data D0 is greater than a preset deviation threshold Y. When the deviation data is greater than the deviation threshold, step 215 is performed; and the deviation data is not greater than the deviation threshold. Then, step 217 is directly executed.
  • the deviation threshold may be set according to the performance of the sensor and the accuracy required by the VR system, which is not limited in the embodiment of the present invention.
  • Step 215 The target sensor data is used as abnormal target sensor data, and the abnormal target sensor data is deleted.
  • Step 217 Collect the normal target sensor data by using the target sensor data as normal target sensor data.
  • the target sensor data D0 is determined as normal data, that is, the target sensor data D0 is used as normal target sensor data, and the target sensor data D0 is reported, that is, The target sensor data D0 is collected and saved.
  • Step 219 Update the sequence based on the normal target sensor data, and recalculate the updated sequence to update the fitting curve.
  • the mobile phone-based VR system may store the normal target sensor data D0 in the sequence LD when the normal target sensor data D0 is collected, that is, update the sequence LD corresponding to the sensor data D; or for each several times ( If the normal target sensor data D0 uploaded is counted every three times, the average value of the normal target sensor data D0 is determined, and the average value is stored.
  • the sequence LD the sequence LD of the sensor data D is updated.
  • the VR system based on the mobile terminal can eliminate the abnormal target sensor data by detecting the coincidence degree of the target sensor data and the fitting curve, thereby solving the problem that the sensor data is abnormal and the image autonomously shifts and the operation focus is difficult.
  • the problem in the point; and the fitting curve is updated based on the normal target sensor data, which ensures the accuracy of the fitting curve and improves the accuracy of the field angle, thereby ensuring the display effect of the image.
  • FIG. 3A a structural block diagram of an embodiment of a processing apparatus for collecting sensor data according to the present invention is shown. Specifically, the following modules may be included:
  • a fit curve generation module 301 can be used to generate a fit curve based on historical sensor data.
  • the target time determining module 303 can be configured to determine a target time corresponding to the target sensor data when the target sensor data is detected.
  • the virtual data calculation module 305 is configured to calculate the target time according to the fitting curve to obtain virtual sensor data corresponding to the target time.
  • the data comparison module 307 can be configured to compare the target sensor data with the virtual sensor data, and exclude abnormal target sensor data according to the comparison result.
  • the sensor data may include, but is not limited to, gyroscope data (such as head orientation) and accelerometer data (such as the magnitude and direction of acceleration received by the mobile phone), and any of the types or types of data. No restrictions.
  • the fitting curve generating module 301 can include a collecting sub-module 3010, a sequence forming sub-module 3012, and an analog computing sub-module 3014, with reference to FIG. 3B.
  • the collection sub-module 3010 is configured to collect sensor data collected by the sensor. Sequence shape The sub-module 3012 is configured to form the sequence by using the collected sensor data as historical sensor data.
  • the simulation calculation sub-module 3014 is configured to perform simulation calculation on the historical sensor data of the sequence to generate a fitting curve.
  • the data comparison module 307 can include the following sub-modules:
  • the deviation determination sub-module 3070 is configured to calculate a difference between the target sensor data and the virtual sensor data, and determine the deviation data.
  • the determining sub-module 3072 is configured to determine whether the deviation data is greater than a preset deviation threshold.
  • the abnormal data exclusion sub-module 3074 is configured to delete the abnormal target sensor data by using the target sensor data as abnormal target sensor data when the deviation data is greater than the deviation threshold.
  • the collecting submodule 3010 may be further configured to collect the target sensor data as normal target sensor data when the deviation data is not greater than the deviation threshold, and collect the normal Target sensor data and trigger the update module.
  • the processing device for collecting sensor data further includes an update module 309.
  • the update module 309 can be configured to update the sequence based on the normal target sensor data, and trigger the simulation calculation sub-module 3014 to recalculate the updated sequence, and update the fitting curve.
  • the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.
  • embodiments of the embodiments of the invention may be provided as a method, apparatus, or computer program product.
  • embodiments of the invention may be in the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware.
  • embodiments of the invention may take the form of a computer program product embodied on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • Figure 4 illustrates that a mobile terminal in accordance with the present invention can be implemented.
  • the mobile terminal transmits
  • the processor 410 and a computer program product or computer readable medium in the form of a memory 420 are included.
  • the memory 420 may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), an EPROM, a hard disk, or a ROM.
  • Memory 420 has a memory space 430 for program code 431 for performing any of the method steps described above.
  • storage space 430 for program code may include various program code 431 for implementing various steps in the above methods, respectively.
  • the program code can be read from or written to one or more computer program products.
  • These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks.
  • Such computer program products are typically portable or fixed storage units as described with reference to FIG.
  • the storage unit may have a storage section, a storage space, and the like arranged similarly to the storage 420 in the mobile terminal of FIG.
  • the program code can be compressed, for example, in an appropriate form.
  • the storage unit includes computer readable code 431', ie, code readable by a processor, such as 410, that when executed by the mobile terminal causes the mobile terminal to perform each of the methods described above step.
  • Embodiments of the invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG.
  • These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing terminal device to produce a machine such that instructions are executed by a processor of a computer or other programmable data processing terminal device
  • Means are provided for implementing the functions specified in one or more of the flow or in one or more blocks of the flow chart.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the instruction device implements the functions specified in one or more blocks of the flowchart or in a flow or block of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device such that a series of operational steps are performed on the computer or other programmable terminal device to produce computer-implemented processing, such that the computer or other programmable terminal device
  • the instructions executed on the instructions are provided for implementation in a block or blocks of a flow or a flow and/or block diagram of the flowchart The steps of the feature.

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Abstract

本发明实施例提供了一种收集传感器数据的处理方法和装置，该法方法包括：基于历史传感器数据生成拟合曲线；当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间；依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据；将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。本发明实施例通过检测目标传感器数据与拟合曲线的吻合程度，剔除异常的目标传感器数据，从而解决传感器数据异常而导致图像自主偏移和操作焦点时难以点中的问题。

Description

一种收集传感器数据的处理方法和装置
本申请要求在2015年12月4日提交中国专利局、申请号为201510885481.3、发明名称为“一种收集传感器数据的处理方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及虚拟现实技术领域，特别是涉及一种收集传感器数据的处理方法和一种收集传感器数据的处理装置。
背景技术
虚拟实境(Virtual Reality，VR)，又称灵境技术或虚拟现实技术，是一种全部或部分由计算机生成的视觉、听觉、触觉等多维感觉环境。通过头盔显示器、数据手套等辅助传感设备，给人提供一个观察并与虚拟环境进行交互作用的多维人机接口，使人可以进入这个虚拟环境中直接观察事物的内在变化并与事物发生交互作用，给人一种“身临其境”的真实感。
随着VR技术的快速发展，基于移动终端的VR影院系统也迅速的发展起来。在基于移动终端的VR影院系统中，传感器数据是视频帧的源头。其中，传感器数据是指安装在传感设备上的传感器所采集到的数据。发明人在实现本发明的过程中发现：传感器数据准确与否，直接关系到计算视场角、根据视场角渲染场景和视频、时间偏差效果(TimeWarp)处理等后续环节。当传感器数据有漂移或者抖动时，基于手机的VR系统生成的图像就可能会自主漂移，即用户可能看到自主漂移的图像，降低了用户体验；并可能引起操作焦点时不易点中的问题。
显然，在现有基于移动终端的VR影院系统中，存在由于传感器数据异常而导致图像自主偏移，以及操作焦点时难以点中的问题。
发明内容
本发明实施例所要解决的技术问题是提供一种收集传感器数据的处理方 法，解决由于传感器数据异常而导致图像自主偏移，以及操作焦点时难以点中的问题。
相应的，本发明实施例还提供了一种收集传感器数据的处理装置，用以保证上述方法的实现及应用。
为了解决上述问题，本发明实施例公开了一种收集传感器数据的处理方法，包括：
基于历史传感器数据生成拟合曲线；
当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间；
依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据；
将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。
相应的，本发明实施例还公开了一种收集传感器数据的处理装置，包括：
拟合曲线生成模块，用于基于历史传感器数据生成拟合曲线；
目标时间确定模块，用于当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间；
虚拟数据计算模块，用于依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据；
数据比较模块，用于将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。
本发明实施例提供一种计算机程序，其包括计算机可读代码，当所述计算机可读代码在移动终端上运行时，导致所述移动终端执行上述的收集传感器数据的处理方法。
本发明实施例提供一种计算机可读介质，其中存储了上述的计算机程序。
与现有技术相比，本发明实施例包括以下优点：
本发明实施例在检测到目标传感器数据时，通过确定目标传感器数据对应的目标时间，并依据拟合曲线对目标时间进行计算，得到目标时间对应的虚拟传感器数据；通过比较目标传感器数据与虚拟传感器数据，依据比较结果排除异常的目标传感器数据，即通过检测目标传感器数据与拟合曲线的吻合程度，剔除异常的目标传感器数据，从而解决传感器数据异常而导致图像自主偏移和操作焦点时难以点中的问题。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明的一种收集传感器数据的处理方法实施例的步骤流程图；
图2是本发明的一种收集传感器数据的处理方法优选实施例的步骤流程图；
图3A是本发明的一种收集传感器数据的处理装置实施例的结构框图；
图3B是本发明的一种收集传感器数据的处理装置优选实施例的结构框图。
图4示意性地示出了用于执行根据本发明的方法的移动终端的框图；以及
图5示意性地示出了用于保持或者携带实现根据本发明的方法的程序代码的存储单元。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
在基于移动终端的VR影院系统中，利用头部跟踪来改变图像的视角，使得用户的视觉系统和运动感知系统之间就可以联系起来，感觉更逼真。通常，可以通过位置追踪器，对用户的头部进行跟踪，确定用户头部的运动状态。其中，位置追踪器是指作用于空间跟踪与定位的装置，一般与其他VR设备结合使用，如：数据头盔、立体眼镜、数据手套等，使参与者在空间上能够自由移动、旋转，不局限于固定的空间位置。实质上，在基于移动终端的VR影院系统中，传感器数据是视频帧的源头。传感器数据准确与否，直接关系到计算视场角、根据视场角渲染场景和视频、TimeWarp处理等后续环节。当传感器数据异常时，就会导致图像自主偏移，以及操作焦点时难以点中的问题。
针对上述问题，本发明实施例的核心构思之一在于，基于历史传感器数据生成拟合曲线，通过检测目标传感器数据与拟合曲线的吻合程度，剔除异常的目标传感器数据，从而解决传感器数据异常而导致图像自主偏移和操作焦点时难以点中的问题。
参照图1，示出了本发明的一种收集传感器数据的处理方法实施例的步骤流程图，具体可以包括如下步骤：
步骤101，基于历史传感器数据生成拟合曲线。
基于移动终端的VR系统可以通过数据头盔、立体眼镜、数据手套等辅助传感设备监测用户头部的移动状态。实际上，基于移动终端的VR系统可以通过监测和收集传感器所采集的数据，确定用户当前的转动状态。具体的，VR系统可以将传感器所采集的数据作为传感器数据，并对收集到的传感器数据进行计算，确定视场角，根据确定的视场角实时渲染图像帧，生成各场景对应的立体图像。需要说明的是，移动终端是指可以在移动中使用的计算机设备，例如智能手机、笔记本电脑、平板电脑等，本发明实施例对此不作限制。本发明实施例将以手机为例进行详细描述。
当基于手机的VR系统收集到传感器数据时，可以将收集到的传感器数据作为历史传感器数据，形成传感器数据的序列，对该序列进行模拟，生成该传感器数据对应的拟合曲线。该拟合曲线可以确定传感器数据与收集该传感器数据的时间的关系，可以将该拟合曲线标注为D＝S(t)。其中，D表示 传感器数据，t表示时间。
在本发明的一种优选实施例中，上述步骤101可以包括以下子步骤：
子步骤1010，收集传感器所采集到传感器数据。
子步骤1012，将所收集到的传感器数据作为历史传感器数据，形成序列。
子步骤1013，对所述序列的历史传感器数据进行模拟计算，生成拟合曲线。
步骤103，当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间。
当VR系统检测到新传感器数据时，可以将所检测到新传感器数据作为目标传感器数据；并可以获取当前时间，将所获取的当前时间作为该目标传感器数据检测到的时间，确定为该目标传感器数据对应的目标时间。例如，基于手机的VR系统检测新传感器数据D0，即目标传感器数据为D0，获取到当前的系统时间为T1，将当前的系统时间T1确定为目标传感器数据D0所对应的目标时间。
步骤105，依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据。
具体的，基于手机的VR系统可以采用拟合曲线对目标时间进行计算，得到该目标时间所对应的理想传感器数据，理想传感器数据也可以称为虚拟传感器数据。结合上述例子，通过拟合曲线D＝S(t)对目标时间T1进行计算，得到目标时间T1对应的理想传感器数据D1，其中D1＝S(t1)，即目标时间T1对应的虚拟传感器数据为D1。
步骤107，将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。
基于手机的VR系统可以预先设置传感器数据的偏差阈值。该偏差阈值用于判断检测到的传感器数据是否为异常数据，即当目标传感器数据与其对应的虚拟传感器数据的偏差大于该偏差阈值时，可以将该目标传感器数据确定为异常数据；当目标传感器数据与其对应的虚拟传感器数据的偏差不大于该偏差阈值时，可以将该目标传感器数据确定为正常数据。具体的，通过计算目标传感器数据与虚拟传感器数据的差值，判断该差值是否大于预置的偏差阈值，当差值大于预置的偏差阈值时将该目标传感器数据抛弃，可以排除 或删除异常的目标传感器数据。如上述例子中，通过计算可以确定目标传感器数据D0与虚拟传感器数据为D1的差值为|D0-D1|，将差值|D0-D1|与预置的偏差阈值Y相比较，当差值|D0-D1|大于偏差阈值Y时，将目标传感器数据D0确定为异常的传感器数据，并删除该目标传感器数据D0。
在本发明的一种优选实施例中，上述步骤107可以包括以下子步骤：
子步骤1070，计算所述目标传感器数据与虚拟传感器数据的差值，确定偏差数据；
子步骤1072，判断所述偏差数据是否大于预置的偏差阈值；
子步骤1072，在所述偏差数据大于偏差阈值时，将所述目标传感器数据作为异常的目标传感器数据，删除所述异常的目标传感器数据。
在本发明实施例中，基于移动终端的VR系统通过确定目标传感器数据对应的目标时间，并依据拟合曲线对目标时间进行计算，可以得到目标时间对应的虚拟传感器数据；将目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据，即通过检测目标传感器数据与拟合曲线的吻合程度，剔除异常的目标传感器数据，从而解决传感器数据异常而导致图像自主偏移和操作焦点时难以点中的问题。
参照图2，示出了本发明的一种收集传感器数据的处理方法实施例的步骤流程图，具体可以包括如下步骤：
步骤201，收集传感器所采集到传感器数据。
基于手机的VR系统通过实时监测用户的状态，依据用户的状态对视频或场景进行渲染，以提高用体验。通常，传感器数据D的上传速度很快，即传感设备可以在一个数据帧的延迟时间内，上传多个传感器数据D到VR系统中。其中，该传感器数据D可以包括但不仅限于陀螺仪数据(如头部朝向)和加速计数据(如手机受到的加速度的大小和方向)等其中任意一类或几类数据，本发明实施例对此不作限制。后文将以传感器数据为一类传感数据为例，对本发明实施例进行描述，但不应将此作为本发明实施例的限制。
针对传感器所上传的传感器数据，基于手机的VR系统可以对进行数据统计，并进行保存，即收集传感器所采集的到传感器数据D。具体的，为了提高所收集的传感器数据的精确度，VR系统可以在监测到传感器所上传的传 感器数据时，针对每X次上传的传感器数据进行计算，得到每X次传感器数据的平均值，并对平均值进行保存。其中，X为整数，如1、2、3等。例如，基于手机的VR系统针对某一类的传感器数据D，通过对每3次上传的传感器数据D进行计算，可以得到每3次上传的同一类传感器数据D的平均值，并对该平均值进行保存。
步骤203，将所收集到的传感器数据作为历史传感器数据，形成序列。
VR系统可以将计算得到平均值作为历史传感器数据Dtn，依次存入相应的序列中，形成该传感器数据D的序列LD。其中，tn表示传感器数据的平均值Dtn生成的时间，相当于VR系统收集该历史传感器数据Dtn的时间，n为整数，如1、2、3、4等。具体的，基于手机的VR系统将在各不同时刻tn(如t1、t2、t3......)所称生传感器数据D的平均值Dtn(如Dt1、Dt2、Dt3......)依次存入相应的状态序列LD中，即形成传感器数据D的序列LD。为了保证渲染图像的效率以及计算得到目标场景的视场角的精确度，优选的将序列LD设置为可以存入30个历史传感器数据的序列，即可以将最新收集的30个历史传感器数据Dtn存入传感器数据D的序列LD。
步骤205，对所述序列的历史传感器数据进行模拟计算，生成拟合曲线。
基于手机的VR系统可通过调用预置的模拟算法对目标状态序列中历史传感器数据Dtn进行模拟计算，生成该序列LD的拟合曲线D＝S(t)。其中，D表示传感器数据，t表示传感器数据D生成的时间，相当于传感器数据对应的时间。
步骤207，当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间。
具体的，基于手机的VR系统在监测到传感器所采集的最新传感器数据时，将最新传感器数据作为目标传感器数据D0，并确定目标传感器数据D0对应的目标时间T1。
步骤209，依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据。
在本发明实施例中，基于手机的VR系统可以将目标时间T1作为tn，采用拟合曲线拟合曲线D＝S(t)对目标时间T1进行计算，得到目标时间T1对应的理想传感器数据D1。
步骤211，计算所述目标传感器数据与虚拟传感器数据的差值，确定偏差数据。
具体的，基于手机的VR系统对目标传感器数据D0与虚拟传感器数据为D1进行计算，得到目标传感器数据D0与虚拟传感器数据为D1的差值|D0-D1|，将差值|D0-D1|作为目标传感器数据D0所对应的偏差数据。
步骤213，判断所述偏差数据是否大于预置的偏差阈值。
基于手机的VR系统可以判断目标传感器数据D0所对应的偏差数据是否大于预置的偏差阈值Y，在所述偏差数据大于偏差阈值时，执行步骤215；在所述偏差数据不大于所述偏差阈值时，直接执行步骤217。其中，偏差阈值可以根据传感器的性能以及VR系统所要求的精确度设置，本发明实施例对此不作限制。
步骤215，将所述目标传感器数据作为异常的目标传感器数据，删除所述异常的目标传感器数据。
具体的，当目标传感器数据D0所对应的偏差数据大于预置的偏差阈值Y时，将目标传感器数据D0确定为异常数据，即目标传感器数据D0作为异常的目标传感器数据，将该异常数据抛弃，即删除该目标传感器数据D0，从而可以避免将异常的传感器数据D上传到传感器数据D的序列LD中，提高拟合曲线D＝S(t)，保证了视场角的精确度，以及图像的显示效果。
步骤217，将所述目标传感器数据作为正常的目标传感器数据，收集所述正常的目标传感器数据。
当目标传感器数据D0所对应的偏差数据不大于预置的偏差阈值Y时，将目标传感器数据D0确定为正常数据，即目标传感器数据D0作为正常的目标传感器数据，将目标传感器数据D0上报，即收集该目标传感器数据D0，对其进行保存。
步骤219，基于所述正常的目标传感器数据更新所述序列，并对更新的序列进行重新计算，更新所述拟合曲线。
具体的，基于手机的VR系统可以在收集到正常的目标传感器数据D0时，将该正常的目标传感器数据D0存入序列LD中，即更新传感器数据D对应的序列LD；或者对每几次(如每3次)所上传的正常目标传感器数据D0进行统计，确定这几次的正常目标传感器数据D0的平均值，将该平均值存入 序列LD中，更新传感器数据D的序列LD。在更新序列LD后，基于手机的VR系统可以通过对更新的序列LD进行重新计算，更新传感器数据D对应的拟合曲线D＝S(t)，进一步保证拟合曲线D＝S(t)的精确度，从而提高视场角的精确度。
在本发明实施例中，基于移动终端的VR系统可以通过检测目标传感器数据与拟合曲线的吻合程度，剔除异常的目标传感器数据，从而解决传感器数据异常而导致图像自主偏移和操作焦点时难以点中的问题；并基于正常的目标传感器数据更新拟合曲线，保证了拟合曲线的精确度，提高场角的精确度，进而保证了图像的显示效果。
需要说明的是，对于方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明实施例并不受所描述的动作顺序的限制，因为依据本发明实施例，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作并不一定是本发明实施例所必须的。
参照图3A，示出了本发明一种收集传感器数据的处理装置实施例的结构框图，具体可以包括如下模块：
拟合曲线生成模块301，可以用于基于历史传感器数据生成拟合曲线。
目标时间确定模块303，可以用于当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间。
虚拟数据计算模块305，可以用于依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据。
数据比较模块307，可以用于将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。
其中，该传感器数据可以包括但不仅限于陀螺仪数据(如头部朝向)和加速计数据(如手机受到的加速度的大小和方向)等其中任意一类或几类数据，本发明实施例对此不作限制。
在图3A的基础上，可选的，拟合曲线生成模块301可以包括收集子模块3010、序列形成子模块3012以及模拟计算子模块3014，参照图3B。
其中，收集子模块3010，用于收集传感器所采集到传感器数据。序列形 成子模块3012，用于将所收集到的传感器数据作为历史传感器数据，形成序列。模拟计算子模块3014，用于对所述序列的历史传感器数据进行模拟计算，生成拟合曲线。
在本发明的一种优选实施例中，数据比较模块307可以包括以下子模块：
偏差确定子模块3070，用于计算所述目标传感器数据与虚拟传感器数据的差值，确定偏差数据。
判断子模块3072，用于判断所述偏差数据是否大于预置的偏差阈值。
异常数据排除子模块3074，用于在所述偏差数据大于偏差阈值时，将所述目标传感器数据作为异常的目标传感器数据，删除所述异常的目标传感器数据。
在本发明的一种优选实施例中，收集子模块3010，还可以用于在所述偏差数据不大于所述偏差阈值时，将所述目标传感器数据作为正常的目标传感器数据，收集所述正常的目标传感器数据，并触发更新模块。该收集传感器数据的处理装置还包括更新模块309。其中，更新模块309可以用于基于所述正常的目标传感器数据更新所述序列，并触发所述模拟计算子模块3014对更新的序列进行重新计算，更新所述拟合曲线。
对于装置实施例而言，由于其与方法实施例基本相似，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
本说明书中的各个实施例均采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似的部分互相参见即可。
本领域内的技术人员应明白，本发明实施例的实施例可提供为方法、装置、或计算机程序产品。因此，本发明实施例可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明实施例可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
例如，图4示出了可以实现根据本发明的移动终端。该移动终端传 统上包括处理器410和以存储器420形式的计算机程序产品或者计算机可读介质。存储器420可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM、硬盘或者ROM之类的电子存储器。存储器420具有用于执行上述方法中的任何方法步骤的程序代码431的存储空间430。例如，用于程序代码的存储空间430可以包括分别用于实现上面的方法中的各种步骤的各个程序代码431。这些程序代码可以从一个或者多个计算机程序产品中读出或者写入到这一个或者多个计算机程序产品中。这些计算机程序产品包括诸如硬盘，紧致盘(CD)、存储卡或者软盘之类的程序代码载体。这样的计算机程序产品通常为如参考图5所述的便携式或者固定存储单元。该存储单元可以具有与图4的移动终端中的存储器420类似布置的存储段、存储空间等。程序代码可以例如以适当形式进行压缩。通常，存储单元包括计算机可读代码431’，即可以由例如诸如410之类的处理器读取的代码，这些代码当由移动终端运行时，导致该移动终端执行上面所描述的方法中的各个步骤。
本发明实施例是参照根据本发明实施例的方法、终端设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理终端设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理终端设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理终端设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理终端设备上，使得在计算机或其他可编程终端设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程终端设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定 的功能的步骤。
尽管已描述了本发明实施例的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例做出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明实施例范围的所有变更和修改。
最后，还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者终端设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者终端设备所固有的要素。在没有更多限制的情况下，由语句“包括一个......”限定的要素，并不排除在包括所述要素的过程、方法、物品或者终端设备中还存在另外的相同要素。
以上对本发明所提供的一种收集传感器数据的处理方法和一种收集传感器数据的处理装置，进行了详细介绍，本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。

Claims (12)

  1. 一种收集传感器数据的处理方法，其特征在于，包括：
    基于历史传感器数据生成拟合曲线；
    当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间；
    依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据；
    将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。
  2. 根据权利要求1所述的方法，其特征在于，所述基于历史传感器数据生成拟合曲线，包括：
    收集传感器所采集到传感器数据；
    将所收集到的传感器数据作为历史传感器数据，形成序列；
    对所述序列的历史传感器数据进行模拟计算，生成拟合曲线。
  3. 根据权利要求2所述的方法，其特征在于，所述将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据，包括：
    计算所述目标传感器数据与虚拟传感器数据的差值，确定偏差数据；
    判断所述偏差数据是否大于预置的偏差阈值；
    在所述偏差数据大于偏差阈值时，将所述目标传感器数据作为异常的目标传感器数据，删除所述异常的目标传感器数据。
  4. 根据权利要求3所述的方法，其特征在于，所述方法还包括：
    在所述偏差数据不大于所述偏差阈值时，将所述目标传感器数据作为正常的目标传感器数据，收集所述正常的目标传感器数据；
    基于所述正常的目标传感器数据更新所述序列，并对更新的序列进行重新计算，更新所述拟合曲线。
  5. 根据权利要求1至4任一所述的方法，其特征在于，所述传感器数据至少包括以下任一项：陀螺仪数据和加速计数据。
  6. 一种收集传感器数据的处理装置，其特征在于，包括：
    拟合曲线生成模块，用于基于历史传感器数据生成拟合曲线；
    目标时间确定模块，用于当检测到目标传感器数据时，确定所述目标传感器数据对应的目标时间；
    虚拟数据计算模块，用于依据所述拟合曲线对所述目标时间进行计算，得到所述目标时间对应的虚拟传感器数据；
    数据比较模块，用于将所述目标传感器数据与虚拟传感器数据进行比较，依据比较结果排除异常的目标传感器数据。
  7. 根据权利要求6所述的装置，其特征在于，所述拟合曲线生成模块，包括：
    收集子模块，用于收集传感器所采集到传感器数据；
    序列形成子模块，用于将所收集到的传感器数据作为历史传感器数据，形成序列；
    模拟计算子模块，用于对所述序列的历史传感器数据进行模拟计算，生成拟合曲线。
  8. 根据权利要求7所述的装置，其特征在于，所述数据比较模块，包括：
    偏差确定子模块，用于计算所述目标传感器数据与虚拟传感器数据的差值，确定偏差数据；
    判断子模块，用于判断所述偏差数据是否大于预置的偏差阈值；
    异常数据排除子模块，用于在所述偏差数据大于偏差阈值时，将所述目标传感器数据作为异常的目标传感器数据，删除所述异常的目标传感器数据。
  9. 根据权利要求8所述的装置，其特征在于，所述收集子模块，还用于在所述偏差数据不大于所述偏差阈值时，将所述目标传感器数据作为正常的目标传感器数据，收集所述正常的目标传感器数据，并触发更新模块；
    所述装置还包括更新模块，所述更新模块用于基于所述正常的目标传感器数据更新所述序列，并触发所述模拟计算子模块对更新的序列进行重新计算，更新所述拟合曲线。
  10. 根据权利要求6至9任一所述的装置，其特征在于，所述传感器数据至少包括以下任一项：陀螺仪数据和加速计数据。
  11. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在移动终端上运行时，导致所述移动终端执行根据权利要求1-5中的任一个所述的收集传感器数据的处理方法。
  12. 一种计算机可读介质，其中存储了如权利要求11所述的计算机程序。
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