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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/26/2024 has/have been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-2, 8-10, 14-15, and 17-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 8, 11, and 13 of U.S. Patent No. 11417017 B2, respectively. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims 1-2, 8-10, 14-15, and 17-18 of instant application can be anticipated by the corresponding claims 1, 4, 8, 11, and 13 of U.S. Patent No. 11417017 B2, respectively.
Instant Application 18785164
U.S. Patent No. 11417017 B2
1. A system, comprising: memory configured to store program instructions; and one or more processors configured to execute the program instructions to: obtain an image from a camera of a vehicle; determine an image feature point based on the image; access a sub-volume (SV) of a map, wherein the SV corresponds to a volume of space in a three-dimensional (3D) environment, and wherein the SV includes a map feature point associated with a feature descriptor; and determine a location of the vehicle based on matching of the image feature point to the map feature point.
9. The system of claim 1, wherein the one or more processors are configured to execute the program instructions to: determine the SV of the map based on an approximate location of the vehicle, wherein the approximate location is less accurate than the determined location of the vehicle.
10. The system of claim 1, wherein the one or more processors are configured to execute the program instructions to: determine whether or not to accept the determined location of the vehicle based on comparison of the determined location to an expected location of the vehicle.
8. A device, comprising: a camera; a memory; one or more processors operatively coupled to the memory, and the camera, wherein the one or more processors are configured to execute non-transitory instructions causing the one or more processors to: obtain an image from the camera; identify a set of image feature points in the image; obtain an approximate location of the device; determine a set of sub-volumes (SVs) of a map to access based on the approximate location of the device, wherein SVs of the set of SVs include a list of the map feature points located within the SV; determine starting locations for SVs of the set of SVs in the map by accessing a map header having indications of locations of SVs in the map; obtain map feature points and associated map feature descriptors of the set of SVs based on the determined starting locations for SVs of the set of SVs; determine a set of candidate matches between the set of image feature points and the obtained map feature points; determine a set of potential poses of the camera from candidate matches from the set of candidate matches and an associated reprojection error estimated for remaining points to select a first pose of the set of potential poses based on having a lowest associated reprojection error; determine the first pose is within a threshold value of an expected device location; and output a device location based on the first pose.
2. The system of claim 1, wherein the image feature point is associated with an image feature descriptor.
8. The system of claim 1, wherein the one or more processors are configured to execute the program instructions to perform the matching of the image feature point to the map feature point based on comparison between a two-dimensional (2D)-3D matched feature point pairs and a 3D-2D matched feature point pairs.
11. The device of claim 8, herein the one or more processors are further configured to determine, for each image feature point in the image, an image feature descriptor associated with the respective image feature point, and wherein the one or more processors are further configured to determine candidate matches of the set of candidate matches between the image feature points and image feature descriptors by causing the one or more processors to: match the image feature descriptor of the image feature point against the obtained of map feature descriptors to determine a two dimensional (2D)-three dimensional (3D) matched feature point pair; match a map feature descriptor of the candidate matched feature point against the image feature descriptors of the set of image feature points to determine a 3D-2D matched feature point pair; and determine a candidate match based on a quality of the match and a comparison between the 2D-3D matched feature point pair and the 3D-2D matched feature point pair.
11. The system of claim 10, wherein the one or more processors are configured to execute the program instructions to: determine the expected location based on a previous location and a motion of the vehicle.
13. The device of claim 8, wherein the expected device location is determined based on a velocity of the device and a maximum permissible motion of the device for a time period.
14. A method, comprising: obtaining an image from a camera of a vehicle; determining an image feature point based on the image; accessing a sub-volume (SV) of a map, wherein the SV corresponds to a volume of space in a three-dimensional (3D) environment, and wherein the SV includes a map feature point associated with a feature descriptor; and determining a location of the vehicle based on matching of the image feature point to the map feature point.
17. The method of claim 14, wherein accessing the SV of the map comprises accessing the SV of the map based on an approximate location of the vehicle, wherein the approximate location is less accurate than the determined location of the vehicle.
18. The method of claim 14, further comprising: determining whether or not accepting the determined location of the vehicle based on an expected location of the vehicle.
1. A method, comprising: obtaining an image from a camera of a vehicle; identifying a set of image feature points in the image; obtaining an approximate location of the vehicle; determining a set of sub-volumes (SVs) of a map to access based on the approximate location of the vehicle, wherein SVs of the set of SVs include a list of the map feature points located within the SV; determining starting locations for SVs of the set of SVs in the map by accessing a map header having indications of locations of SVs in the map; obtaining map feature points and associated map feature descriptors of the set of SVs based on the determined starting locations for SVs of the set of SVs; determining a set of candidate matches between the set of image feature points and the obtained map feature points; determining a set of potential poses of the camera from candidate matches from the set of candidate matches and an associated reprojection error estimated for remaining points to select a first pose of the set of potential poses based on having a lowest associated reprojection error; determining the first pose is within a threshold value of an expected vehicle location; and outputting a vehicle location based on the first pose.
15. The method of claim 14, wherein the image feature point is associated with an image feature descriptor.
4. The method of claim 1, further comprising determining, for each image feature point of the set of image feature points in the image, an image feature descriptor associated with the respective image feature point, and wherein determining candidate matches of the set of candidate matches between the image feature points and the obtained map feature points comprises: matching the image feature descriptor of the image feature point against the obtained of map feature descriptors to determine a two dimensional (2D)-three dimensional (3D) matched feature point pair; matching a map feature descriptor of the candidate matched feature point against the image feature descriptors of the set of image feature points to determine a 3D-2D matched feature point pair; and determining a candidate match based on a quality of the match and a comparison between the 2D-3D matched feature point pair and the 3D-2D matched feature point pair.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 9-11, 14-15, 17-18 is/are rejected under 35 U.S.C. 102(a)92) as being anticipated by Zhang et al (US 10395117 B1), hereinafter Zhang.
-Regarding claim 1, Zhang discloses a system, comprising (Abstract; FIGS. 1-42): memory configured to store program instructions; and one or more processors configured to execute the program instructions to (FIGS. 1-3, 11-12, 15-16, 18-19; 22-24; 26-28): obtain an image from a camera of a vehicle (FIGS 1-4; Col. 11, lines 15-20, “robot … mobile platform … vehicle …cameras 208, 210”; Col. 11, lines 40-63, “image frames captured by cameras 208, 210”, “receives a raw image”; FIG. 11, step 1110; FIGS. 12-13, 15-16; 23-25); determine an image feature point based on the image (FIGS. 1, 3-5A, 8-10, 12-13, 18-19, 23, 27, 32; Col. 8, lines 41-59, “feature undistortion … feature points … feature detection …”; Col. 9, lines 6-15, “feature description … identify … match … feature points”; Col. 9, lines 29-41, “identify the feature points”); access a sub-volume (SV) of a map, wherein the SV corresponds to a volume of space in a three-dimensional (3D) environment (FIG. 4, map processor 414; FIG. 12, steps1250, 1270; FIGS. 22, 37-38; Col. 17, lines 12-36, “a query of the map … are map points (xyz, uncertainty, average reprojection error, etc.), keyrigs' poses, 2D-3D constraint information, and occupancy grid …”; Col. 60, lines 10-24, “map server receives … a set of keyrigs … with a pose generated using … GPS, IMU … generates … 3D map using the set of keyrigs”), and wherein the SV includes a map feature point associated with a feature descriptor (FIGS. 22, 28, 34-36; Col. 20, lines7-13; Col. 39, lines 57-63, “ORB descriptor”; Col. 58, lines 36-61, “map entry for a feature point … a longitude 3404, a latitude 3402, and an ORB feature description 3406 … include additional elements”; Col. 43, lines 20-28, “sparse 3D map builder 2203 with visual data … identifies objects in the visual data … the feature description engine …”); and determine a location of the vehicle based on matching of the image feature point to the map feature point (FIGS. 4, 5A-5B; FIG. 12, steps 1270-1290; FIGS. 24-25; Col. 29, lines 49-62, “the extracted new features are matched to the retrieved feature points based on … matching of features in the new frame with reprojected feature positions from the 3D map onto a 2D view from a perspective of the propagated pose, producing a list of matching features … corrected pose is calculated using positions of the matching features …”).
-Regarding claim 14, Zhang discloses a method, comprising (Abstract; FIGS. 1-42): obtaining an image from a camera of a vehicle (FIGS 1-4; Col. 11, lines 15-20, “robot … mobile platform … vehicle …cameras 208, 210”; Col. 11, lines 40-63, “image frames captured by cameras 208, 210”, “receives a raw image”; FIG. 11, step 1110; FIGS. 12-13, 15-16; 23-25); determining an image feature point based on the image (FIGS. 1, 3-5A, 8-10, 12-13, 18-19, 23, 27, 32; Col. 8, lines 41-59, “feature undistortion … feature points … feature detection …”; Col. 9, lines 6-15, “feature description … identify … match … feature points”; Col. 9, lines 29-41, “identify the feature points”); accessing a sub-volume (SV) of a map, wherein the SV corresponds to a volume of space in a three-dimensional (3D) environment (FIG. 4, map processor 414; FIG. 12, steps1250, 1270; FIGS. 22, 37-38; Col. 17, lines 12-36, “a query of the map … are map points (xyz, uncertainty, average reprojection error, etc.), keyrigs' poses, 2D-3D constraint information, and occupancy grid …”; Col. 60, lines 10-24, “map server receives … a set of keyrigs … with a pose generated using … GPS, IMU … generates … 3D map using the set of keyrigs”), and wherein the SV includes a map feature point associated with a feature descriptor (FIGS. 22, 28, 34-36; Col. 20, lines7-13; Col. 39, lines 57-63, “ORB descriptor”; Col. 58, lines 36-61, “map entry for a feature point … a longitude 3404, a latitude 3402, and an ORB feature description 3406 … include additional elements”; Col. 43, lines 20-28, “sparse 3D map builder 2203 with visual data … identifies objects in the visual data … the feature description engine …”); and determining a location of the vehicle based on matching of the image feature point to the map feature point (FIGS. 4, 5A-5B; FIG. 12, steps 1270-1290; FIGS. 24-25; Col. 29, lines 49-62, “the extracted new features are matched to the retrieved feature points based on … matching of features in the new frame with reprojected feature positions from the 3D map onto a 2D view from a perspective of the propagated pose, producing a list of matching features … corrected pose is calculated using positions of the matching features …”).
-Regarding claims 2 and 15, Zhang discloses the system of claim 1 and the method of claim 14. Zhang further discloses wherein the image feature point is associated with an image feature descriptor (FIGS. 1, 18; Col. 39, lines 49-63).
-Regarding claim 3, Zhang further discloses to perform the matching of the image feature point to the map feature point based on a cost function on the image feature descriptor of the image feature point and the feature descriptor of the map feature point (FIG. 5A-5B; Col. 39, lines 59-60, “The description of a feature can be used for matching purposes and describe a feature's uniqueness”; Col. 29, lines 49-54, “the extracted new features are matched to the retrieved feature points based on … matching of features in the new frame with reprojected feature positions from the 3D map …”; Col. 41, lines ).
-Regarding claims 9 and 17, Zhang disclose the system of claim 1 and the method of claim 14. Zhang further discloses to determine the SV of the map based on an approximate location of the vehicle, wherein the approximate location is less accurate than the determined location of the vehicle (FIG. 4, map processor 414; FIG. 12, steps1250, 1270; FIGS. 22, 37-38; Col. 17, lines 12-36, “a query of the map … are map points (xyz, uncertainty, average reprojection error, etc.), keyrigs' poses, 2D-3D constraint information, and occupancy grid …”; Col. 60, lines 10-24, “map server receives … a set of keyrigs … with a pose generated using … GPS, IMU … generates … 3D map using the set of keyrigs”; Col. 37, lines 18-20, “IMU 1510 which runs at relatively high frequency to provide frequent updates of less accurate information …”).
-Regarding claims 10 and 18, Zhang disclose the system of claim 1 and the method of claim 14. Zhang further discloses to determine whether or not to accept the determined location of the vehicle based on comparison of the determined location to an expected location of the vehicle (FIG. 5A, step 570; Col. 19, lines 12-31, “threshold”; FIG. 12, step 1280; FIG. 13, steps 1310-1320; Col. 16, line 49 – Col. 17, line 5, Col. 31, lines 22-37, “threshold”; Col. 17, lines 40-48; Col. 21, lines 1-23).
-Regarding claims 11, Zhang disclose the system of claim 10. Zhang further discloses to determine the expected location based on a previous location and a motion of the vehicle (FIG. 1, optical flow feature correspondence processor 118; FIG. 4; Col. 14, lines 37-39, “calculate the motion between two image frames, taken at times t and t+Δt at each voxel position …”; FIG, 18, processor 1812; Col. 16 lines 62-64, “use previous time period velocity/acceleration to predict a pose”).
Claim Rejections - 35 USC § 103
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.
Claim(s) 4-5, 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US 10395117 B1), hereinafter Zhang in view of Desai et al (IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 7, 2016, pp. 1839-1851), hereinafter Desai.
-Regarding claim 4, Zhang discloses the system of claim 3.
Zhang does not disclose wherein the cost function is a sum of absolute differences (SAD) algorithm.
In the same field of endeavor, Desai teaches feature point matching using a feature descriptor (Desai: Abstract; FIGS. 1-9; Equations (1)-(5)). Desai further teaches wherein the cost function is a sum of absolute differences (SAD) algorithm (Desai: page 1841, Sec. C., 1st paragraph; equation (4)).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Desai by using sum of absolute differences (SAD) algorithm for feature points matching with feature descriptor in order to reduce computation complexity (Desai: page 1841, Sec. C., 1st paragraph).
-Regarding claim 5, Zhang discloses the system of claim 2.
Zhang does not disclose wherein the image feature descriptor represents a relationship between the image feature point and a region surrounding the image feature point.
In the same field of endeavor, Desai teaches feature point matching using a feature descriptor (Desai: Abstract; FIGS. 1-9; Equations (1)-(5)). Desai further teaches wherein the image feature descriptor represents a relationship between the image feature point and a region surrounding the image feature point (Desai: FIG. 2; Page 1842, 1st Col., 3rd paragraph, “All detected features are saved on the feature list. A small image region surrounding a detected feature point on the feature list, called feature region image (FRI) is cropped and saved as a30× 30 FRI”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Desai by using an image feature descriptor representing a relationship between the image feature point and a region surrounding the image feature point in order to provide good feature matching accuracy without performing complex descriptor calculations (Desai: page 1841, 2nd Col., last paragraph).
-Regarding claim 7, Zhang discloses the system of claim 2.
Zhang does not disclose to determine the image feature point, the image feature descriptor, or a combination thereof based on one or more algorithms including: speeded up robust features (SURF), scale-invariant feature transform (SIFT), vantage point (VP) tree, oriented features from accelerated segment test (oriented FAST), rotated binary robust independent elementary features (rotated FAST), or Kaze features.
In the same field of endeavor, Desai teaches feature point matching using a feature descriptor (Desai: Abstract; FIGS. 1-9; Equations (1)-(5)). Desai further teaches to determine the image feature point, the image feature descriptor, or a combination thereof based on one or more algorithms including: speeded up robust features (SURF), scale-invariant feature transform (SIFT), vantage point (VP) tree, oriented features from accelerated segment test (oriented FAST), rotated binary robust independent elementary features (rotated FAST), or Kaze features (Desai: Page 1840, 1st Col., 2nd paragraph, “… SURF, and SIFT…”, 4th paragraph; Page 1842, 1st Col., 2nd paragraph, “selected SURF … Using SURF …for feature descriptors to operate …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Desai by using on one or more algorithms including: speeded up robust features (SURF), scale-invariant feature transform (SIFT), vantage point (VP) tree, oriented features from accelerated segment test (oriented FAST), rotated binary robust independent elementary features (rotated FAST), or Kaze features in order to provide a fairness comparison with these popular and convenient algorithms (Desai: Desai: Page 1842, 1st Col., 2nd paragraph).
-Regarding claim 16, Zhang discloses the method of claim 15. Zhang further discloses to perform the matching of the image feature point to the map feature point based on a cost function on the image feature descriptor of the image feature point and the feature descriptor of the map feature point (FIG. 5A-5B; Col. 39, lines 59-60, “The description of a feature can be used for matching purposes and describe a feature's uniqueness”; ; Col. 29, lines 49-54, “the extracted new features are matched to the retrieved feature points based on … matching of features in the new frame with reprojected feature positions from the 3D map …”; Col. 41, lines ).
Zhang does not disclose wherein the cost function is a sum of absolute differences (SAD) algorithm.
In the same field of endeavor, Desai teaches feature point matching using a feature descriptor (Desai: Abstract; FIGS. 1-9; Equations (1)-(5)). Desai further teaches wherein the cost function is a sum of absolute differences (SAD) algorithm (Desai: page 1841, Sec. C., 1st paragraph; equation (4)).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Desai by using sum of absolute differences (SAD) algorithm for feature points matching with feature descriptor in order to reduce computation complexity (Desai: page 1841, Sec. C., 1st paragraph).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US 10395117 B1), hereinafter Zhang in view of in view of Du et al (US 20180210321 A1), hereinafter Du.
-Regarding claim 6, Zhang discloses the system of claim 1.
Zhang does not disclose wherein the image feature descriptor comprises one or more properties of the image feature point including: a chroma value, a luma value, a grayscale value, a derivative of chroma values, a derivative of luma values, or a derivative of grayscale values.
In the same field of endeavor, Du teaches a method an autonomous moving device, including a camera and a camera heating device to reduce affecting by the weather and the geographical environment (Du: Abstract; FIGS. 1-8). [5] further teaches wherein the image feature descriptor comprises one or more properties of the image feature point including: a chroma value, a luma value, a grayscale value, a derivative of chroma values, a derivative of luma values, or a derivative of grayscale values (Du: FIGS. 3, 5-7; [0098], “a local image feature descriptor that may be quickly calculated for … a grayscale value, a color histogram, a grayscale histogram, and a grayscale moment …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Du by including grayscale in an image feature descriptor in order to more accurate obtain local information of the image (Du: [0098]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US 10395117 B1), hereinafter Zhang in view of in view of Hou et al (US 20180005015 A1), hereinafter Hou.
-Regarding claim 8, Zhang discloses the system of claim 1. Zhang further discloses to determine a two dimensional (2D)-three dimensional (3D) matched feature point pair (Zhang: FIG. 5A, step 540; FIG. 12, step 1270); and determine a 3D-2D matched feature point pair (Zhang: FIG. 28, map builders 2803, 2804).
Zhang does not disclose determining a candidate match based on a comparison between the 2D-3D matched feature point pair and the 3D-2D matched feature point pair.
In the same field of endeavor, Hou teaches a method for tracking a pose of one or more objects represented in a scene (Hou: Abstract; FIGS. 1-17B). Hou further teaches determining a candidate match based on a quality of the match and a comparison between the 2D-3D matched feature point pair and the 3D-2D matched feature point pair (Hou: FIG. 2; [0064], “using 3D+2D searching”, “perform an alternative 2D+3D searching”, “refined by RANSAC to maximize the number of inliers that meet a 3D distance and 2D re-projection error requirement”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the teaching of Hou by determining a candidate match based on a quality of the match and a comparison between the 2D-3D matched feature point pair and the 3D-2D matched feature point pair in order to improve the accuracy and reliability of matching.
Allowable Subject Matter
Claims 12-13 and 19-20 are 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, and overcome nonstatutory double patenting rejection in above section of “Double Patenting”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ishigami (U.S PG-PUB NO. 20180033160 A1), hereinafter Ishigami teaches determining a group of voxels in association with the space represented by a map. Ishigami further teaches wherein a sub-volumes (SV) of the set of SVs include a list of the map feature points located within the sub-volume.
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/XIAO LIU/Primary Examiner, Art Unit 2664
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