Office Action 1: Non-Final Rejection (2024-03-22)
Rejection types: 103 - Obviousness

CLAIMS:
CLAIMS

What is claimed is:

1\. A computer-implemented method comprising the operations of:
obtaining vehicle dimension data describing dimensions of a vehicle;
moving a robotic apparatus to a first position about the vehicle;
determining a first reference point of the vehicle; and
causing the robotic apparatus to move to a second position about the vehicle based on the determined first reference point.

2\. The method of claim 1, wherein the determined first reference point is a center point of a first wheel of the vehicle.

3\. The method of claim 2, further comprising the operations of:
aligning a coordinate system of the robotic apparatus to the determined first reference point of the first wheel of the vehicle; and
based on the aligned coordinate system of the robotic apparatus, performing operations by the robotic apparatus to remove lug nuts from a first wheel hub of the vehicle.

4\. The method of claim 2, further comprising the operations of:
moving the robotic apparatus to a second position about a second wheel of the vehicle;
determining a second reference point of the second wheel of the vehicle; and
causing the robotic apparatus to move to positions about the second wheel of the vehicle based on the determined second reference point.

5\. The method of claim 4, wherein the determined second reference point is a center point of the second wheel of the vehicle.

6\. The method of claim 5, further comprising the operations of:
aligning a coordinate system of the robotic apparatus to the determined second reference point of the second wheel; and based on the aligned coordinate system of the robotic apparatus, performing operations by the robotic apparatus to remove lug nuts from a second wheel hub of the vehicle.

7\. The method of claim 1, wherein obtaining the vehicle dimension information comprises:
obtaining a vehicle identification number associated with the vehicle;
accessing a database that comprises information related to a plurality of vehicles; and
obtaining from the vehicle dimension data from the database based on the vehicle identification number.

8\. The method of claim 7, wherein the vehicle dimension information includes at least one of dimensions of a wheel base of the vehicle and dimensions of a track width of the vehicle.

9\. The method of claim 1, wherein determining a first reference point of the vehicle comprises:
aligning a plane of a digital camera with a plane of a first wheel of the vehicle having a center point; and
moving the digital camera to a position where a center point of the camera is aligned with a center point of the first wheel.

10\. The method of claim 1, wherein determining a first reference point of the first wheel of the vehicle comprises:
obtaining, via one or more cameras, digital imagery of a first wheel of the vehicle, the first wheel having a plurality of lug nuts and a wheel center;
determining, using the digital imagery, a movement offset of a then current position of the robotic apparatus;
moving the robotic apparatus from the then current position to a new position based on the determined movement offset; and
determining whether the one or more cameras is aligned to the wheel center.
11\. The method of claim 10, wherein determining a first reference point of the first wheel of the vehicle comprises:
wherein the robotic apparatus has a digital camera; and
determining one or more movement offset values and moving the robotic apparatus based on the one or more movement offset values until an alignment point of the robotic apparatus is aligned to a center point of the first wheel, the alignment point being a center point of the digital camera.

12\. The method of claim 11, wherein the movement offset comprises a coordinate values for an x, y, z, rX, and rY offset, wherein rY is a rotation value about the y axis value, and
rX is a rotation value about the x axis value.

13\. The method of claim 11, wherein the determining using the digital imagery comprises the operations of wherein the digital imagery comprises 2D imagery and a 3D point cloud of the first wheel;
inputting into a machine learning model the 2D imagery and generating a plurality of bounding boxes associated with the lug nuts of the first wheel and determining a number of the lug nuts of the first wheel;
using the generated plurality of bounding boxes with the 3D point cloud to define an x, y, z coordinate information for each of the plurality of lug nuts; and
calculating an average Z distance of the lug nuts.

14\. A system comprising:
a robotic apparatus adapted to perform a maintenance task on a vehicle;
a processing device communicatively coupled to the robotic apparatus, the processing device comprising:
a memory storing computer program instructions; and
a processor communicatively coupled to the memory, the processor configured to execute the computer program instructions which, when executed on the processor, cause the processor to perform operations comprising: obtaining vehicle dimension data describing dimensions of a vehicle;
causing the robotic apparatus to move to a first position about the vehicle;
determining a first reference point of the vehicle; and
causing the robotic apparatus to move to a second position about the vehicle based on the determined first reference point.

15\. The system of claim 14, wherein the determined first reference point is a center point of a first wheel of the vehicle.

16\. The system of claim 15, the operations further comprising:
aligning a coordinate system of the robotic apparatus to the determined first reference point of the first wheel of the vehicle; and
based on the aligned coordinate system of the robotic apparatus, causing the robotic apparatus to perform operations to remove lug nuts from a first wheel hub of the vehicle.

17\. The system of claim 14, the operations further comprising:
causing the robotic apparatus to move to a second position about a second wheel of the vehicle;
determining a second reference point of the second wheel of the vehicle; and
causing the robotic apparatus to move to positions about the second wheel of the vehicle based on the determined second reference point.

18\. The system of claim 17, wherein the determined second reference point is a center point of the second wheel of the vehicle.

19\. The system of claim 18, the operations further comprising:
aligning a coordinate system of the robotic apparatus to the determined second reference point of the second wheel; and
based on the aligned coordinate system of the robotic apparatus, causing the robotic apparatus to perform operations to remove lug nuts from a second wheel hub of the vehicle. 54 55 56 57

OFFICE ACTION:
DETAILED ACTION
Claims 1-19 are pending.
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 .
Notice on Prior Art Rejections
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-AFA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not he 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.
Priority
Acknowledgment is made of applicant’s claim for priority of U.S provisional application under 35 U.S.C. 119(e).
Acknowledgment is made of the claim for priority or continuation and divisional applications under 35 U.S.C. 120.
Information Disclosure Statement
The information disclosure statements provided complies with the provisions of MPEP § 609.  It has been placed in the application file, and the information referred to therein has been considered as to the merits.  A signed copy of the form is attached.
Specification
The abstract of the disclosure is objected to because of the phrase “may be”.  Correction is required.  See MPEP § 608.01(b).
Examiner's Note
Examiner has cited particular paragraphs/ columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary.  Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ricci (US 10,354,460).
As per claim 1, Ricci teaches a computer-implemented method comprising the operations of: obtaining vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) dimension data describing dimensions of a vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); moving a robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to a first position about the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); determining a first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); and causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to move to a second position about the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) based on the determined first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice). Although, the above limitations are disclosed from different embodiments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of different embodiments of Ricci into the intended end result, thereby improving the robotic apparatus interaction with vehicle based on vehicle dimension translation as a whole.
It is well settled that it is obviousness in the overall appearance of the claimed invention, when compared with the prior art, rather than minute details or small variations in the claims as appears to be the case here, that constitutes the test of patentability.
As per claim 2, Ricci teaches wherein the determined first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) is a center point of a first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein the vehicle has one or more wheel).
As per claim 3, Ricci further comprising the operations of: aligning a coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to the determined first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); and based on the aligned coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus, performing operations by the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to remove lug nuts from a first wheel hub of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein removing lug nuts falls under design choice).
As per claim 4, Ricci further comprising the operations of: moving the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to a second position about a second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); determining a second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); and causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to move to positions about the second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) based on the determined second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice).
As per claim 5, Ricci teaches wherein the determined second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) is a center point of the second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100).
As per claim 6, Ricci further comprising the operations of: aligning a coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700 and col. 22, lines 34-39) apparatus to the determined second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the second wheel; and based on the aligned coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus, performing operations by the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to remove lug nuts from a second wheel hub of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein removing lug nuts falls under design choice).
As per claim 7, Ricci teaches wherein obtaining the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) dimension information comprises: obtaining a vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) identification number associated with the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); accessing a database that comprises information related to a plurality of vehicle (see Fig. 2, 4A, 4B and 6-12 element 100)s; and obtaining from the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) dimension data from the database based on the vehicle identification number (see Fig. 2, 4A, 4B and 6-12 element 100).
As per claim 8, Ricci teaches wherein the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) dimension information includes at least one of dimensions of a wheel base of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) and dimensions of a track width of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100).
As per claim 9, Ricci teaches wherein determining a first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) comprises: aligning a plane of a digital camera (see col. 51, lines 13-20) with a plane of a first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) having a center point; and moving the digital camera (see col. 51, lines 13-20) to a position where a center point of the camera is aligned with a center point of the first wheel (see col. 51, lines 13-20, and having a camera aligned a center of a wheel falls under design choice).
As per claim 10, Ricci teaches wherein determining a first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) comprises: obtaining, via one or more camera (see col. 51, lines 13-20)s, digital imagery (see col. 51, lines 13-20) of a first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100), the first wheel having a plurality of lug nuts and a wheel center (see Fig. 20, it is known for the wheel to have lug nuts and a center wheel); determining, using the digital imagery (see col. 51, lines 13-20), a movement offset of a then current position of the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus; moving the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus from the then current position to a new position based on the determined movement offset; and determining whether the one or more cameras is aligned to the wheel center (see col. 51, lines 13-20).
As per claim 11, Ricci teaches wherein determining a first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) comprises: wherein the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus has a digital camera (see col. 51, lines 13-20); and determining one or more movement offset values and moving the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus based on the one or more movement offset values until an alignment point of the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus is aligned to a center point of the first wheel, the alignment point being a center point of the digital camera (see col. 51, lines 13-20).
As per claim 13, Ricci teaches wherein the determining using the digital imagery (see col. 51, lines 13-20) comprises the operations of wherein the digital imagery (see col. 51, lines 13-20) comprises 2D imagery and a 3D point cloud of the first wheel (see Fig. 20, it is known for the wheel to have lug nuts and a center wheel; and col. 20, lines 17-22); inputting into a machine learning model the 2D imagery and generating a plurality of bounding boxes associated with the lug nuts of the first wheel and determining a number of the lug nuts of the first wheel (see Fig. 20, it is known for the wheel to have lug nuts and a center wheel); using the generated plurality of bounding boxes with the 3D point cloud to define an x, y, z coordinate information for each of the plurality of lug nuts; and calculating an average Z distance of the lug nuts (see Fig. 20, it is known for the wheel to have lug nuts and a center wheel; and col. 20, lines 17-22 as noted above).
As per claim 14, Ricci teaches a system comprising: a robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus adapted to perform a maintenance task on a vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); a processing device communicatively coupled to the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus, the processing device comprising: a memory storing computer program instructions (see Fig. 2, wherein two way communication is being performed by means of computerized system); and a processor communicatively coupled to the memory, the processor configured to execute the computer program instructions which, when executed on the processor, cause the processor to perform operations comprising (see Fig. 2, wherein two way communication is being performed by means of computerized system as noted above): obtaining vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) dimension data describing dimensions of a vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to move to a first position about the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); determining a first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); and causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to move to a second position about the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein performing a task on any of the wheels falls under design choice) based on the determined first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice).  Although, the above limitations are disclosed from different embodiments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of different embodiments of Ricci into the intended end result, thereby improving the robotic apparatus interaction with vehicle based on vehicle dimension translation as a whole.
It is well settled that it is obviousness in the overall appearance of the claimed invention, when compared with the prior art, rather than minute details or small variations in the claims as appears to be the case here, that constitutes the test of patentability.
As per claim 15, Ricci teaches wherein the determined first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) is a center point of a first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein performing a task on any of the wheels falls under design choice).
As per claim 16, Ricci teaches the operations further comprising: aligning a coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700 and col. 22, lines 34-39) apparatus to the determined first reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the first wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein performing a task on any of the wheels falls under design choice); and based on the aligned coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus, causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to perform operations to remove lug nuts from a first wheel hub of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein removing lug nuts falls under design choice).
As per claim 17, Ricci teaches the operations further comprising: causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to move to a second position about a second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein performing a task on any of the wheels falls under design choice); determining a second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100); and causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to move to positions about the second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100) based on the determined second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice).
As per claim 18, Ricci teaches wherein the determined second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) is a center point of the second wheel of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein performing a task on any of the wheels falls under design choice).
As per claim 19, Ricci teaches the operations further comprising: aligning a coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700 and col. 22, lines 34-39) apparatus to the determined second reference point (see Fig. 7, wherein the end effector of the robot can move from point to point by design choice) of the second wheel (see Fig. 2, 4A, 4B and 6-12 element 100, wherein performing a task on any of the wheels falls under design choice); and based on the aligned coordinate system of the robotic (see Fig. 2, element 254 and Fig. 7, element 700 and col. 22, lines 34-39) apparatus, causing the robotic (see Fig. 2, element 254 and Fig. 7, element 700) apparatus to perform operations to remove lug nuts from a second wheel hub of the vehicle (see Fig. 2, 4A, 4B and 6-12 element 100, wherein removing lug nuts falls under design choice).
Allowable Subject Matter
Claim 12 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record fails to teach or fairly suggest wherein the movement offset comprises a coordinate values for an x, y, z, rX, and rY offset, wherein rY is a rotation value about the y axis value, and rX is a rotation value about the x axis value.
Conclusion


NEXT OFFICE ACTION: Allowable Subject Matter
Claims 1-20 are allowed in light of the amendment filed on 12/23/2024.
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 .
The following is an examiner’s statement of reasons for allowance:
The prior art does not anticipate nor render obvious the claimed subject matter of:
A computer-implemented method comprising the operations of: obtaining vehicle dimension data describing dimensions of a vehicle; causing, based at least in part on the vehicle dimension data, a robotic apparatus to move to a first position about the vehicle to collect data regarding a selected wheel of the vehicle; determining a first reference point of the vehicle based at least in part on the collected data regarding the selected wheel; and causing the robotic apparatus to move to a second position about the vehicle based on the determined first reference point.
No particular single feature of the claim renders the claim as a whole patentable. Only the claim taken as a whole combination is deemed new and unobvious.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US11175132B2: Claims:
A system comprising: one or more processors; and one or more computer readable storage media communicatively coupled to the one or more processors and storing instructions that are executable by the one or more processors to: receive sensor data captured by a plurality of sensors of a vehicle; apply the sensor data to a voxel space; determine a first pose of a first sensor of the plurality of sensors and a second pose of a second sensor of the plurality of sensors; identify a transform to apply to the first sensor or the second sensor based at least in part on a set of perturbations, wherein: the transform comprises at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, and perturbation in the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation of the first sensor or the at least one of the second translation or the second rotation of the second sensor; calculate a residual value of the voxel space associated with each perturbation applied to the first sensor or the second sensor; and determine an optimized pose of the first sensor or the second sensor based at least in part on an optimized residual value of the voxel space, the optimized residual value corresponding to the transform associated with one of the set of perturbations.
one or more processors; and
one or more computer readable storage media communicatively coupled to the one or more processors and storing instructions that are executable by the one or more processors to: receive sensor data captured by a plurality of sensors of a vehicle; apply the sensor data to a voxel space; determine a first pose of a first sensor of the plurality of sensors and a second pose of a second sensor of the plurality of sensors; identify a transform to apply to the first sensor or the second sensor based at least in part on a set of perturbations, wherein: the transform comprises at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, and perturbation in the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation of the first sensor or the at least one of the second translation or the second rotation of the second sensor; calculate a residual value of the voxel space associated with each perturbation applied to the first sensor or the second sensor; and determine an optimized pose of the first sensor or the second sensor based at least in part on an optimized residual value of the voxel space, the optimized residual value corresponding to the transform associated with one of the set of perturbations.
receive sensor data captured by a plurality of sensors of a vehicle;
apply the sensor data to a voxel space;
determine a first pose of a first sensor of the plurality of sensors and a second pose of a second sensor of the plurality of sensors;
identify a transform to apply to the first sensor or the second sensor based at least in part on a set of perturbations, wherein: the transform comprises at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, and perturbation in the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation of the first sensor or the at least one of the second translation or the second rotation of the second sensor;
the transform comprises at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, and
perturbation in the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation of the first sensor or the at least one of the second translation or the second rotation of the second sensor;
calculate a residual value of the voxel space associated with each perturbation applied to the first sensor or the second sensor; and
determine an optimized pose of the first sensor or the second sensor based at least in part on an optimized residual value of the voxel space, the optimized residual value corresponding to the transform associated with one of the set of perturbations.
The system of claim 1 , wherein the orientation dimension corresponds to a pitch, roll, and yaw associated with the first sensor or the second sensor.
The system of claim 1 , wherein the at least one of the directional dimension corresponds to a translation of the first sensor or the second sensor with respect to an X-axis, a Y-axis, or a Z-axis.
The system of claim 1 , wherein the magnitude of each perturbation is based at least in part on a voxel size of individual voxels in the voxel space.
The system of claim 1 , wherein the set of perturbations comprises combinations of orientations and no change to the directional dimension.
The system of claim 1 , further comprising: determining at least one magnitude to apply to the set of perturbations; and applying the at least one magnitude to the set of perturbations, wherein the optimized residual value is based at least in part on an optimal value resulting from an application of the at least one magnitude to the set of perturbations.
determining at least one magnitude to apply to the set of perturbations; and
applying the at least one magnitude to the set of perturbations,
wherein the optimized residual value is based at least in part on an optimal value resulting from an application of the at least one magnitude to the set of perturbations.
The system of claim 1 , wherein the instructions are further executable by the one or more processors to calibrate at least one of the first sensor or the second sensor based at least in part on the optimized pose.
The system of claim 1 , wherein the instructions are further executable by the one or more processors to determine a drift error associated with a position of the vehicle based at least in part on the optimized pose.
A method comprising: receiving a dataset comprising first sensor data captured by a first sensor and second sensor data captured by a second sensor of a vehicle, the first sensor being associated with a first pose and the second sensor being associated with a second pose; applying the dataset to a voxel space; determining a residual value of the voxel space; and identifying a transform to apply to at least one of the first sensor or the second sensor based at least in part on a set of perturbations associated with at least one of the first sensor data or the second sensor data, wherein: the transform represents at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, a perturbation of the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation or the at least one of the second translation or the second rotation, and the transform is identified based at least in part on an optimized residual value associated with the set of perturbations.
receiving a dataset comprising first sensor data captured by a first sensor and second sensor data captured by a second sensor of a vehicle, the first sensor being associated with a first pose and the second sensor being associated with a second pose;
applying the dataset to a voxel space;
determining a residual value of the voxel space; and
identifying a transform to apply to at least one of the first sensor or the second sensor based at least in part on a set of perturbations associated with at least one of the first sensor data or the second sensor data, wherein: the transform represents at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, a perturbation of the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation or the at least one of the second translation or the second rotation, and the transform is identified based at least in part on an optimized residual value associated with the set of perturbations.
the transform represents at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose,
a perturbation of the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation or the at least one of the second translation or the second rotation, and
the transform is identified based at least in part on an optimized residual value associated with the set of perturbations.
The method of claim 9 , wherein the perturbation comprises perturbing the directional dimension independently of the orientation dimension.
The method of claim 9 , wherein the dataset is a first dataset, the method further comprising: receiving a first indication that the vehicle is at a reference point at a first time; based at least in part on the first indication, capturing the first dataset comprising a first representation of an environment proximate to the reference point; receiving a second indication that the vehicle is at the reference point at a second time; based at least in part on the second indication, capturing a second dataset comprising a second representation of the environment proximate to the reference point; and wherein identifying the transform is based at least in part on the second dataset.
receiving a first indication that the vehicle is at a reference point at a first time;
based at least in part on the first indication, capturing the first dataset comprising a first representation of an environment proximate to the reference point;
receiving a second indication that the vehicle is at the reference point at a second time;
based at least in part on the second indication, capturing a second dataset comprising a second representation of the environment proximate to the reference point; and
wherein identifying the transform is based at least in part on the second dataset.
The method of claim 9 , further comprising: calibrating at least one of the first sensor or the second sensor based at least in part on the transform; or determining a drift error of the vehicle based at least in part on the transform.
calibrating at least one of the first sensor or the second sensor based at least in part on the transform; or
determining a drift error of the vehicle based at least in part on the transform.
The method of claim 9 , further comprising determining a size of voxels in the voxel space, wherein the perturbation is based at least in part on the size of the voxels.
The method of claim 9 , wherein the orientation dimension corresponds to a pitch, roll, and yaw associated with the first sensor or the second sensor.
The method of claim 9 , wherein the directional dimension corresponds to a translation of the first sensor or the second sensor with respect to an X-axis, a Y-axis, or a Z-axis.
One or more non-transitory computer-readable storage media communicatively coupled to one or more processors and storing instructions that are executable by the one or more processors to: receive a dataset comprising first sensor data captured by a first sensor and second sensor data captured by a second sensor, the first sensor being associated with a first pose and the second sensor being associated with a second pose; project the dataset into a voxel space; determine a residual value of the voxel space; and identify a transform to apply to at least one of the first sensor or the second sensor based at least in part on a set of perturbations associated with at least one of the first sensor data or the second sensor data, wherein: the transform represents at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, a perturbation of the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation or to the at least one of the second translation or the second rotation, and the transform is identified based at least in part on an optimized residual value associated with the set of perturbations.
receive a dataset comprising first sensor data captured by a first sensor and second sensor data captured by a second sensor, the first sensor being associated with a first pose and the second sensor being associated with a second pose;
project the dataset into a voxel space;
determine a residual value of the voxel space; and
identify a transform to apply to at least one of the first sensor or the second sensor based at least in part on a set of perturbations associated with at least one of the first sensor data or the second sensor data, wherein: the transform represents at least one of a first translation or a first rotation of the first sensor from the first pose or at least one of a second translation or a second rotation of the second sensor from the second pose, a perturbation of the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation or to the at least one of the second translation or the second rotation, and the transform is identified based at least in part on an optimized residual value associated with the set of perturbations.
a perturbation of the set of perturbations comprises a magnitude and at least one of a directional dimension or an orientation dimension corresponding to the at least one of the first translation or the first rotation or to the at least one of the second translation or the second rotation, and
The one or more non-transitory computer-readable storage media of claim 16 , wherein the instructions are further executable by the one or more processors to: calibrate at least one of the first sensor or the second sensor based at least in part on the transform; or determine a drift error of a vehicle based at least in part on the transform.
calibrate at least one of the first sensor or the second sensor based at least in part on the transform; or
determine a drift error of a vehicle based at least in part on the transform.
The one or more non-transitory computer-readable storage media of claim 16 , wherein: the orientation dimension corresponds to at least one of a pitch, a roll, or a yaw; and the directional dimension corresponds to at least one of an X-axis, a Y-axis, or a Z-axis.
the orientation dimension corresponds to at least one of a pitch, a roll, or a yaw; and
the directional dimension corresponds to at least one of an X-axis, a Y-axis, or a Z-axis.
The one or more non-transitory computer-readable storage media of claim 16 , wherein the perturbation comprises modifications to two or three dimensions simultaneously.
The one or more non-transitory computer-readable storage media of claim 16 , wherein a magnitude of the transform is based at least in part on a voxel size of individual voxels in the voxel space.
Ref US10579062B2: Claims:
A computer-implemented method for generating a reference path for operating an autonomous driving vehicle (ADV), the method comprising: generating a first vehicular path based on a map information; collecting data points representing geographical coordinates of vehicles that drove on the first vehicular path at different points in time; segmenting the first vehicular path into discrete path segments based on the collected data points, wherein the discrete path segments are stored as key-value pairs and each key-value pair represents an interconnection by a key path segment to a value path segment, wherein each of the collected data points includes a first dimension value and a second dimension value; for each of the path segments, applying a smoothing function to select a subset of the collected data points that are within a predetermined proximity of the corresponding path segment, wherein the smoothing function comprises a two-dimensional Gaussian blur function to apply smoothing to a first and a second dimension of the selected data points of the collected data points; and calculating a segment reference point to represent the path segment by combining the selected data points, wherein the segment reference points of the path segments are interpolated to generate a second vehicular path; and controlling an ADV on the first vehicular path using the second vehicular path as a reference line.
generating a first vehicular path based on a map information;
collecting data points representing geographical coordinates of vehicles that drove on the first vehicular path at different points in time;
segmenting the first vehicular path into discrete path segments based on the collected data points, wherein the discrete path segments are stored as key-value pairs and each key-value pair represents an interconnection by a key path segment to a value path segment, wherein each of the collected data points includes a first dimension value and a second dimension value;
for each of the path segments, applying a smoothing function to select a subset of the collected data points that are within a predetermined proximity of the corresponding path segment, wherein the smoothing function comprises a two-dimensional Gaussian blur function to apply smoothing to a first and a second dimension of the selected data points of the collected data points; and calculating a segment reference point to represent the path segment by combining the selected data points, wherein the segment reference points of the path segments are interpolated to generate a second vehicular path; and
applying a smoothing function to select a subset of the collected data points that are within a predetermined proximity of the corresponding path segment, wherein the smoothing function comprises a two-dimensional Gaussian blur function to apply smoothing to a first and a second dimension of the selected data points of the collected data points; and
calculating a segment reference point to represent the path segment by combining the selected data points, wherein the segment reference points of the path segments are interpolated to generate a second vehicular path; and
controlling an ADV on the first vehicular path using the second vehicular path as a reference line.
The method of claim 1 , wherein the first vehicular path is generated based on midpoints of a vehicular path lane of the map information.
The method of claim 1 , wherein the smoothing is applied to the first dimension independent of the smoothing applied to the second dimension of the selected data points.
The method of claim 1 , wherein applying 
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