DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20, as originally filed, are currently pending and have been considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo, Xiaoting, et al. "Vision and dual IMU integrated attitude measurement system." 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems. Vol. 10621. SPIE, 2018, hereinafter, “Guo”, and further in view of Foxlin, Eric, et al. "Flighttracker: A novel optical/inertial tracker for cockpit enhanced vision." Third IEEE and ACM international symposium on mixed and augmented reality. IEEE, 2004, hereinafter, “Foxlin”.
As per claim 1, Guo discloses an object tracking method, adapted to a host (Guo pages 5-6, 4. Evaluation Experiments of the Integrated System, fast object motion tracking), comprising:
determining a reference motion state based on a first predicted motion state and a calibration factor (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as : 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias; Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained);
obtaining a first motion data and a second motion data (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base);
determining a first relative pose of the [second] object relative to the [master] based on the first motion data, the second motion data, and the reference motion state (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base. As the determination output of inertial sensor is relative to inertial frame, thus angular rate of the master IMU includes not only motion of the measured object relative to inertial frame but also the rocking base relative to inertial frame … The proposed integrated attitude determination system is tested on practical experimental platform; Guo, pages 3-4, 2.2 Principle of dual IMU system, Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2] ... where m im ω , m ib ω , m bm ω stand for the angular rate of master IMU relative to inertial frame, the angular rate of rocking base relative to inertial frame, and the angular rate of master IMU relative to rocking base respectively. The i, m, b represent the inertial frame, the master IMU, and the rocking base accordingly ... For attitude determination on rocking base, our purpose is to obtain the relative attitude between measured moving object namely the turntable relative to rocking base ... the rotation matrix calibration is completed and the pure angular rate can be obtained); and
determining a specific pose of the [second] object based on the first relative pose (Guo, pages 3-4, 2.2 Principle of dual IMU system, Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2] ... where m im ω , m ib ω , m bm ω stand for the angular rate of master IMU relative to inertial frame, the angular rate of rocking base relative to inertial frame, and the angular rate of master IMU relative to rocking base respectively. The i, m, b represent the inertial frame, the master IMU, and the rocking base accordingly ... For attitude determination on rocking base, our purpose is to obtain the relative attitude between measured moving object namely the turntable relative to rocking base ... the rotation matrix calibration is completed and the pure angular rate can be obtained).
Guo does not explicitly disclose the following limitations as further recited however Foxlin discloses
obtaining a first motion data of the host and a second motion data of a reference object (Foxlin, pages 2-3, 2. Differential Inertial Measurement, compute the changes in orientation and position of a pilot’s helmet relative to the aircraft by combining inertial data from a helmet-mounted tracking IMU and an aircraft-mounted reference IMU. This produces a differential inertial measurement of the head motion relative to the aircraft; Foxlin, pages 3-4, 2.2. Inertial tracking relative to an arbitrary maneuvering platform, tracking a person’s head relative to a maneuvering platform by using “differential inertial measurements” between a first inertial measurement unit (IMU) mounted on the helmet and a second fixed on the aircraft frame ... The first step to achieving the desirable tracking system illustrated in Figure 2 is to choose the n-frame fixed to the moving platform ... the b-frame is defined fixed in the body being tracked, normally the helmet, with the x-axis forward, y-axis right, and z-axis down. The n-frame is now a moving frame fixed relative to the aircraft … we will consider the n-frame to have its origin at the reference IMU and axes aligned with the reference IMU axes, as illustrated in Figure 3. We wish to track the b-frame relative to the n-frame, which itself is moving relative to the inertial reference frame).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Foxlin and Guo because they are in the same field of endeavor. One skilled in the art would have been motivated to substitute the host relative to the reference as taught by Foxlin for the master relative to the rocking base as taught by Guo as an alternative means to determine motion and pose between two objects moving relative to each other (Foxlin, pages 2-3, 2. Differential Inertial Measurement).
As per claim 2, Guo and Foxlin disclose the method according to claim 1, further comprising: obtaining a specific gain, a visual relative pose of the reference object relative to the host and a motion relative pose of the reference object relative to the host; determining the calibration factor based on the specific gain, the visual relative pose, and the motion relative pose (Guo, page 2, 2.1 Vision attitude determination, During the whole determination process, the stereo target will be captured by the camera and a series of target images at different pose will be obtained, where each image contains unique attitude information; Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias).
As per claim 3, Guo and Foxlin disclose the method according to claim 2, further comprising: obtaining a first reference gain factor, the first predicted motion state, and the visual relative pose, and accordingly determining the specific gain (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix).
As per claim 4, Guo and Foxlin disclose the method according to claim 3, further comprising: updating the first reference gain factor based on the specific gain and the first predicted motion state (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix).
As per claim 5, Guo and Foxlin disclose the method according to claim 2, wherein the step of determining the calibration factor based on the specific gain, the visual relative pose, and the motion relative pose comprises: determining a pose difference between the visual relative pose and the motion relative pose (Guo, pages 2-3, 2.1 Vision attitude determination, During the whole determination process, the stereo target will be captured by the camera and a series of target images at different pose will be obtained, where each image contains unique attitude information ... To increase the determination accuracy of vision method, a pose algorithm based on constraint calibration is employed ... The specific calculation process of constraint calibration method can be divided into two steps: calibration and determination. In the calibration procedure, the position of the whole space is collected and calibrated. And a one-to-one correspondence among the reference location, reference attitude and space base vector is established; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix); and
determining the calibration factor based on the specific gain and the pose difference (Guo, page 9, 5. Conclusions, A new integrated system fusing vision and dual IMU is designed and applied for attitude determination on rocking base. To improve the calculation accuracy, constrained calibration algorithm is used to calculate vision attitude. For dual IMU system, the initial rotation matrix needs to know. And the two vector determinations method is employed to complete the calibration. EKF method is used to fuse inertial and vision data together by filter).
As per claim 6, Guo and Foxlin disclose the method according to claim 1, wherein the step of determining the reference motion state based on the first predicted motion state and the calibration factor comprises: determining the reference motion state via combining the first predicted motion state with the calibration factor (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as : 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias; Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained).
As per claim 7, Guo and Foxlin disclose the method according to claim 1, wherein the step of determining the first relative pose of the reference object relative to the host based on the first motion data, the second motion data, and the reference motion state comprises:
determining a second predicted motion state based on the first motion data, the second motion data, and the reference motion state, wherein the second predicted motion state comprises the first relative pose and parameters associated with the first motion data and the second motion data (Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias [this is an iterative algorithm].
As per claim 8, Guo and Foxlin disclose the method according to claim 7, wherein the first motion data is collected by a first motion detection circuit on the host, the second motion data is collected by a second motion detection circuit on the reference object (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base; Foxlin, Abstract, cockpit helmet-tracking for enhanced/synthetic vision by implementing algorithms for differential inertial tracking between helmet-mounted and aircraft-mounted inertial sensors);
wherein the parameters associated with the first motion data comprise intrinsic and extrinsic parameters associated with the first motion detection circuit; wherein the parameters associated with the second motion data comprise intrinsic and extrinsic parameters associated with the second motion detection circuit (Guo, page 3, 2.2 Principle of dual IMU system, the output of inertial sensor is relative to inertial frame. A MEMS IMU usually consists of triaxial accelerometers, triaxial gyroscopes, and sometimes even triaxial magnetometers. Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2]; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... Considering bias and noise, the output model of gyro can be built up as: [Equation 9]; Guo, Abstract, To determination relative attitude between two space objects on a rocking base).
As per claim 9, Guo and Foxlin disclose the method according to claim 1, further comprising: obtaining an updated reference gain factor and a second predicted motion state; determining a second reference gain factor based on the updated reference gain factor and the second predicted motion state (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias [this is an iterative algorithm]).
As per claim 10, Guo and Foxlin disclose the method according to claim 1, wherein the step of determining the specific pose of the reference object based on the first relative pose comprises: obtaining a specific relative pose of the host relative to a reference coordinate system (Guo, pages 3-4, 2.2 Principle of dual IMU system, Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2] ... where m im ω , m ib ω , m bm ω stand for the angular rate of master IMU relative to inertial frame, the angular rate of rocking base relative to inertial frame, and the angular rate of master IMU relative to rocking base respectively. The i, m, b represent the inertial frame, the master IMU, and the rocking base accordingly ... For attitude determination on rocking base, our purpose is to obtain the relative attitude between measured moving object namely the turntable relative to rocking base);
determining the specific pose of the reference object via combining the specific relative pose with the first relative pose (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base. As the determination output of inertial sensor is relative to inertial frame, thus angular rate of the master IMU includes not only motion of the measured object relative to inertial frame but also the rocking base relative to inertial frame).
As per claim 11, Guo discloses a [master], comprising:
a non-transitory storage circuit, storing a program code; and a processor, coupled to the non-transitory storage circuit and accessing the program code (Guo, pages 5-6, 4. Evaluation Experiments of the Integrated System, image processing procedure, which improves computational efficiency of the integrated system) to perform:
determining a reference motion state based on a first predicted motion state and a calibration factor (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as : 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias; Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained);
obtaining a first motion data of the [master] and a second motion data of a [second] object (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base);
determining a first relative pose of the [second] object relative to the [master] based on the first motion data, the second motion data, and the [second] motion state (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base. As the determination output of inertial sensor is relative to inertial frame, thus angular rate of the master IMU includes not only motion of the measured object relative to inertial frame but also the rocking base relative to inertial frame … The proposed integrated attitude determination system is tested on practical experimental platform; Guo, pages 3-4, 2.2 Principle of dual IMU system, Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2] ... where m im ω , m ib ω , m bm ω stand for the angular rate of master IMU relative to inertial frame, the angular rate of rocking base relative to inertial frame, and the angular rate of master IMU relative to rocking base respectively. The i, m, b represent the inertial frame, the master IMU, and the rocking base accordingly ... For attitude determination on rocking base, our purpose is to obtain the relative attitude between measured moving object namely the turntable relative to rocking base ... the rotation matrix calibration is completed and the pure angular rate can be obtained); and
determining a specific pose of the [second] object based on the first relative pose (Guo, pages 3-4, 2.2 Principle of dual IMU system, Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2] ... where m im ω , m ib ω , m bm ω stand for the angular rate of master IMU relative to inertial frame, the angular rate of rocking base relative to inertial frame, and the angular rate of master IMU relative to rocking base respectively. The i, m, b represent the inertial frame, the master IMU, and the rocking base accordingly ... For attitude determination on rocking base, our purpose is to obtain the relative attitude between measured moving object namely the turntable relative to rocking base ... the rotation matrix calibration is completed and the pure angular rate can be obtained).
Guo does not explicitly disclose the following limitations as further recited however Foxlin discloses
a host (Foxlin, pages 2-3, 2. Differential Inertial Measurement, compute the changes in orientation and position of a pilot’s helmet relative to the aircraft by combining inertial data from a helmet-mounted tracking IMU and an aircraft-mounted reference IMU);
obtaining a first motion data of the host and a second motion data of a reference object (Foxlin, pages 2-3, 2. Differential Inertial Measurement, compute the changes in orientation and position of a pilot’s helmet relative to the aircraft by combining inertial data from a helmet-mounted tracking IMU and an aircraft-mounted reference IMU. This produces a differential inertial measurement of the head motion relative to the aircraft; Foxlin, pages 3-4, 2.2. Inertial tracking relative to an arbitrary maneuvering platform, tracking a person’s head relative to a maneuvering platform by using “differential inertial measurements” between a first inertial measurement unit (IMU) mounted on the helmet and a second fixed on the aircraft frame ... The first step to achieving the desirable tracking system illustrated in Figure 2 is to choose the n-frame fixed to the moving platform ... the b-frame is defined fixed in the body being tracked, normally the helmet, with the x-axis forward, y-axis right, and z-axis down. The n-frame is now a moving frame fixed relative to the aircraft … we will consider the n-frame to have its origin at the reference IMU and axes aligned with the reference IMU axes, as illustrated in Figure 3. We wish to track the b-frame relative to the n-frame, which itself is moving relative to the inertial reference frame).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Foxlin and Guo because they are in the same field of endeavor. One skilled in the art would have been motivated to substitute the host relative to the reference as taught by Foxlin for the master relative to the rocking base as taught by Guo as an alternative means to determine motion and pose between two objects moving relative to each other (Foxlin, pages 2-3, 2. Differential Inertial Measurement).
As per claim 12, Guo and Foxlin disclose the host according to claim 11, wherein the processor further performs: obtaining a specific gain, a visual relative pose of the reference object relative to the host and a motion relative pose of the reference object relative to the host; determining the calibration factor based on the specific gain, the visual relative pose, and the motion relative pose (Guo, page 2, 2.1 Vision attitude determination, During the whole determination process, the stereo target will be captured by the camera and a series of target images at different pose will be obtained, where each image contains unique attitude information; Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias).
As per claim 13, Guo and Foxlin disclose the host according to claim 12, wherein the processor further performs: obtaining a first reference gain factor, the first predicted motion state, and the visual relative pose, and accordingly determining the specific gain (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix).
As per claim 14, Guo and Foxlin disclose the host according to claim 13, wherein the processor further performs: updating the first reference gain factor based on the specific gain and the first predicted motion state (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix).
As per claim 15, Guo and Foxlin disclose the host according to claim 12, wherein the processor performs: determining a pose difference between the visual relative pose and the motion relative pose (Guo, pages 2-3, 2.1 Vision attitude determination, During the whole determination process, the stereo target will be captured by the camera and a series of target images at different pose will be obtained, where each image contains unique attitude information ... To increase the determination accuracy of vision method, a pose algorithm based on constraint calibration is employed ... The specific calculation process of constraint calibration method can be divided into two steps: calibration and determination. In the calibration procedure, the position of the whole space is collected and calibrated. And a one-to-one correspondence among the reference location, reference attitude and space base vector is established; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix); and
determining the calibration factor based on the specific gain and the pose difference (Guo, page 9, 5. Conclusions, A new integrated system fusing vision and dual IMU is designed and applied for attitude determination on rocking base. To improve the calculation accuracy, constrained calibration algorithm is used to calculate vision attitude. For dual IMU system, the initial rotation matrix needs to know. And the two vector determinations method is employed to complete the calibration. EKF method is used to fuse inertial and vision data together by filter).
As per claim 16, Guo and Foxlin disclose the host according to claim 11, wherein the processor performs determining the reference motion state via combining the first predicted motion state with the calibration factor (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as : 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias; Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained).
As per claim 17, Guo and Foxlin disclose the host according to claim 11, wherein the processor performs: determining a second predicted motion state based on the first motion data, the second motion data, and the reference motion state, wherein the second predicted motion state comprises the first relative pose and parameters associated with the first motion data and the second motion data (Guo, pages 3-4, 2.2 Principle of dual IMU system, the calibration of initial rotation matrix is necessary. And two vector determinations method is applied for calibration of the rotation matrix here. Control the rocking base moving to one direction, the motion information expressed in dual IMU can be expressed as 1 ωm and 1 ωs respectively. Control the rocking base moving to another direction, the output of the dual IMU is 2 ωm and 2 ω s respectively. According to these two determinations, two sets of vectors can be obtained ... the rotation matrix calibration is completed and the pure angular rate can be obtained; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias [this is an iterative algorithm].
As per claim 18, Guo and Foxlin disclose the host according to claim 17, wherein the first motion data is collected by a first motion detection circuit on the host, the second motion data is collected by a second motion detection circuit on the reference object (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base; Foxlin, Abstract, cockpit helmet-tracking for enhanced/synthetic vision by implementing algorithms for differential inertial tracking between helmet-mounted and aircraft-mounted inertial sensors);
wherein the parameters associated with the first motion data comprise intrinsic and extrinsic parameters associated with the first motion detection circuit; wherein the parameters associated with the second motion data comprise intrinsic and extrinsic parameters associated with the second motion detection circuit (Guo, page 3, 2.2 Principle of dual IMU system, the output of inertial sensor is relative to inertial frame. A MEMS IMU usually consists of triaxial accelerometers, triaxial gyroscopes, and sometimes even triaxial magnetometers. Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2]; Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... Considering bias and noise, the output model of gyro can be built up as: [Equation 9]; Guo, Abstract, To determination relative attitude between two space objects on a rocking base).
As per claim 19, Guo and Foxlin disclose the host according to claim 11, wherein the processor further performs: obtaining an updated reference gain factor and a second predicted motion state; determining a second reference gain factor based on the updated reference gain factor and the second predicted motion state (Guo, pages 4-5, 3. Fusion Attitude Algorithm Based on Error Quaternion EKF, For fusion attitude calculation, when vision data is available, vision data and angular rate are fused by EKF ... With filter state x = [xq, xb] ... initial state, and initial covariance matrix P, the filter process can be described as: 1) Propagation of state vector ... 2) One step prediction of state ... 3) One step prediction of covariance matrix ... 4) Update filter gain ... 5) Update state ...6) Update covariance matrix ... 7) Update quaternion and bias).
As per claim 20, Guo and Foxlin disclose the host according to claim 11, wherein the processor performs: obtaining a specific relative pose of the host relative to a reference coordinate system (Guo, pages 3-4, 2.2 Principle of dual IMU system, Based on this theory, under the circumstances of the rocking base swinging at random for master IMU, the angular rate of its gyro output can be interpreted as [Equation 2] ... where m im ω , m ib ω , m bm ω stand for the angular rate of master IMU relative to inertial frame, the angular rate of rocking base relative to inertial frame, and the angular rate of master IMU relative to rocking base respectively. The i, m, b represent the inertial frame, the master IMU, and the rocking base accordingly ... For attitude determination on rocking base, our purpose is to obtain the relative attitude between measured moving object namely the turntable relative to rocking base);
determining the specific pose of the reference object via combining the specific relative pose with the first relative pose (Guo, Abstract, To determination relative attitude between two space objects on a rocking base, an integrated system based on vision and dual IMU (inertial determination unit) is built up. The determination system fuses the attitude information of vision with the angular determinations of dual IMU by extended Kalman filter (EKF) to obtain the relative attitude. One IMU (master) is attached to the measured motion object and the other (slave) to the rocking base. As the determination output of inertial sensor is relative to inertial frame, thus angular rate of the master IMU includes not only motion of the measured object relative to inertial frame but also the rocking base relative to inertial frame).
Conclusion
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668