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 Claims
This Office Action is in response to the application filed on April 23rd, 2024. Claims 1-15 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on April 23rd, 2024 and November 14th, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to KR10-2023-0059938 dated May 9th, 2023. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e). Failure to provide a certified translation may result in no benefit being accorded for the non-English application. No action is required on the part of the applicant at this time.
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities:
Claims 1, 8 and 15 recite “acquiring first point cloud data including distance and reflection intensity information using a 2D LiDAR sensor of a mobile robot” should recite “acquiring first point cloud data including distance and reflection intensity information using a 2D LiDAR sensor of the mobile robot”.
Claims 1, 8 and 15 recite “estimating a pose of the target object based on change in the target cluster” should recite “estimating the pose of the target object based on change in the target cluster.”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 13 recite the limitation "the points of the point cloud data" and “the number of the points…in the first group” and “the number of the points…in the second group”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-5, 8-12, and 15 are rejected under 35 U.S.C. 102(a)(1) as anticipated by US-20190108647 (hereinafter, “Lee”).
Regarding claim 1 Lee discloses a method for estimating a pose of a target object (see at least [0013]; “Embodiments of the present disclosure provide systems and methods for identifying one or more objects (e.g., distinguishing objects from other objects, determining a position of an object, determining an orientation of an object) using intensity and range information obtained via a detector.”) using a 2D LiDAR-based marker (see at least [0013]; “a Laser/Light Detection and Ranging (LADAR/LIDAR) sensor that collects both intensity information and range information may be utilized”) in a mobile robot (see at least [0037]; “the system 100 may be used in connection with docking of aircraft or spacecraft”), the method comprising:
acquiring first point cloud data including distance and reflection intensity information using a 2D LiDAR sensor of a mobile robot (see at least [0022]; “the depicted detector 120 is configured to acquire both range information and intensity information, which may be represented in separate images as shown in fig. 2and 3,” and [0041]; “range information and intensity information is acquired with a detector…the range information is 3-dimensional in nature and corresponds to the distance from the detector of the portions of an environment including the object that are within a field of view of the detector,” the 3-dimensional information of the surrounding environment corresponds to first point cloud data);
extracting second point cloud data corresponding to a high-brightness reflective material included in a marker attached to an object from the first point cloud data (see at least [0026]; “after successful initial thresholding is performed, the majority of the intensity image is removed leaving primarily reflectors or targets of interest in the intensity image. Next, the remaining pixels having a value of 1 may be analyzed to separate reflectors from non-target pixels,” the remaining pixels correspond to the second point cloud data);
clustering the second point cloud data into at least one cluster (see at least [0026]; “after a successful initial thresholding, the majority of the intensity image is removed leaving primarily reflectors or targets of interest”);
identifying a target cluster corresponding to the target object among the at least one cluster (see at least [0013]; “identifying one or more objects (e.g., distinguishing objects from other objects, determining a position of an object, determining an orientation of an object) using intensity and range information obtained via a detector,” [0017]; “For example, different sizes and/or shapes of reflectors may be used on different objects, with each object having a unique size and/or shape of reflector for conveniently distinguishing between different objects based on reflector size and/or shape. As another example, different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors. It may be noted that in various embodiments, uniformly sized and shaped reflectors may be employed” the information received from the detectors is analyzed to differentiate between objects, information corresponding to specific reflectors are grouped together as being associated with a singular object); and
estimating a pose of the target object based on change in the target cluster (see at least [0045]; “At 422, the object is identified using the correlated locations of the reflectors. With the spatial relationship of the array of detectors to the object known a priori, once the location of each reflector is determined, the position and location of the array may be determined, and accordingly the position and location of the object may be determined, as well as the orientation of the object, based on the determined reflector positions and the knowledge of the spatial relationship between the reflectors and the object. Identification of the object in various embodiments includes one or more of distinguishing the object from other objects, identifying a location of the object, or identifying an orientation of the object. For example, at 424, a pose (position and orientation) of the object is determined.”).
Regarding claim 2 Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein the marker attached to the object comprises a plurality of high-brightness reflective materials having different sizes (see at least [0016-0017]; “the reflectors 110 are arranged in an array 112 on the object 102. The reflectors are an example of a marker that may be disposed in an environment on an object. Four reflectors are shown in the illustrated embodiment; however it may be noted that more or less reflectors may be used in various embodiments….the reflectors 110 may be configured in one or more shapes….different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors”).
Regarding claim 3 Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein the extracting comprises extracting, as the second point cloud data, point cloud data having a value of reflection intensity equal or greater than a threshold among the first point cloud data (see at least [0025]; “For example, an initial detection of reflectors may be performed using the intensity information. As the reflectors 110 are generally brighter than other objects in the intensity image 200, the intensity information may be thresholded to create a binary image. The threshold value in various embodiments is a predetermined or precalculated value based on the reflective material used for the reflectors 110 and expected sensor performance.”).
Regarding claim 4 Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein the clustering comprises performing distance-based clustering on the second point cloud data (see at least [0026]; “the pixels may be organized into groups of adjacent pixels,” whether pixels are adjacent to one another is based on distance).
Regarding claim 5 Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein the identifying comprises comparing a shape of the at least one cluster with a preset shape to identify the target cluster corresponding to the target object (see at least [0017]; “The reflectors 110 may be configured in one or more shapes. For example, the reflectors 110 may have a hemispherical shape, with the curved portion forming the reflective surface 111, and the flat surface configured for mounting to the object 102. A reflector 110 having a hemispherical shape will generally reflect a circular pattern of light or other wave (e.g., infrared (IR)) regardless of the angle from which the light or other wave impacts the reflector 110. Other shapes may be used additionally or alternatively. For example, different sizes and/or shapes of reflectors may be used on different objects, with each object having a unique size and/or shape of reflector for conveniently distinguishing between different objects based on reflector size and/or shape. As another example, different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors. It may be noted that in various embodiments, uniformly sized and shaped reflectors may be employed,” and [0040]; “additional information such as size and/or shape of reflector may be cataloged or tabulated for each reflector. A group of cataloged information describing the reflectors attached to a given object may be cataloged for each object to be analyzed).
Regarding claim 8 Lee discloses a device for estimating a pose of a target object (see at least [0013]; “Embodiments of the present disclosure provide systems and methods for identifying one or more objects (e.g., distinguishing objects from other objects, determining a position of an object, determining an orientation of an object) using intensity and range information obtained via a detector.”) using a 2D LiDAR-based marker in a mobile robot (see at least [0013]; “a Laser/Light Detection and Ranging (LADAR/LIDAR) sensor that collects both intensity information and range information may be utilized”), the device comprising:
at least one memory storing computer-executable instructions (see at least Fig. 1; memory 132); and
at least one processor (see at least Fig. 1; processing 130), wherein the at least one processor is configured to execute the computer-executable instructions to:
acquire first point cloud data including distance and reflection intensity information using a 2D LiDAR sensor of a mobile robot (see at least [0022]; “the depicted detector 120 is configured to acquire both range information and intensity information, which may be represented in separate images as shown in fig. 2and 3,” and [0041]; “range information and intensity information is acquired with a detector…the range information is 3-dimensional in nature and corresponds to the distance from the detector of the portions of an environment including the object that are within a field of view of the detector,” the 3-dimensional information of the surrounding environment corresponds to first point cloud data);
extract second point cloud data corresponding to a high-brightness reflective material included in a marker attached to an object from the first point cloud data (see at least [0026]; “after successful initial thresholding is performed, the majority of the intensity image is removed leaving primarily reflectors or targets of interest in the intensity image. Next, the remaining pixels having a value of 1 may be analyzed to separate reflectors from non-target pixels,” the remaining pixels correspond to the second point cloud data);
cluster the second point cloud data into at least one cluster (see at least [0026]; “after a successful initial thresholding, the majority of the intensity image is removed leaving primarily reflectors or targets of interest”);
identify a target cluster corresponding to the target object among the at least one cluster (see at least [0013]; “identifying one or more objects (e.g., distinguishing objects from other objects, determining a position of an object, determining an orientation of an object) using intensity and range information obtained via a detector,” [0017]; “For example, different sizes and/or shapes of reflectors may be used on different objects, with each object having a unique size and/or shape of reflector for conveniently distinguishing between different objects based on reflector size and/or shape. As another example, different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors. It may be noted that in various embodiments, uniformly sized and shaped reflectors may be employed” the information received from the detectors is analyzed to differentiate between objects, information corresponding to specific reflectors are grouped together as being associated with a singular object); and
estimate a pose of the target object based on change in the target cluster (see at least [0045]; “At 422, the object is identified using the correlated locations of the reflectors. With the spatial relationship of the array of detectors to the object known a priori, once the location of each reflector is determined, the position and location of the array may be determined, and accordingly the position and location of the object may be determined, as well as the orientation of the object, based on the determined reflector positions and the knowledge of the spatial relationship between the reflectors and the object. Identification of the object in various embodiments includes one or more of distinguishing the object from other objects, identifying a location of the object, or identifying an orientation of the object. For example, at 424, a pose (position and orientation) of the object is determined.”).
Regarding claim 9 Lee discloses all of the limitations of claim 8. Additionally, Lee discloses wherein the marker attached to the object comprises a plurality of high-brightness reflective materials having different sizes (see at least [0016-0017]; “the reflectors 110 are arranged in an array 112 on the object 102. The reflectors are an example of a marker that may be disposed in an environment on an object. Four reflectors are shown in the illustrated embodiment; however it may be noted that more or less reflectors may be used in various embodiments….the reflectors 110 may be configured in one or more shapes….different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors”).
Regarding claim 10 Lee discloses all of the limitations of claim 8. Additionally, Lee discloses wherein the at least one processor is configured to extract, as the second point cloud data, point cloud data having a value of reflection intensity equal or greater than a threshold among the first point cloud data (see at least [0025]; “For example, an initial detection of reflectors may be performed using the intensity information. As the reflectors 110 are generally brighter than other objects in the intensity image 200, the intensity information may be thresholded to create a binary image. The threshold value in various embodiments is a predetermined or precalculated value based on the reflective material used for the reflectors 110 and expected sensor performance.”).
Regarding claim 11 Lee discloses all of the limitations of claim 8. Additionally, Lee discloses wherein the at least one processor is configured to perform distance-based clustering on the second point cloud data (see at least [0026]; “the pixels may be organized into groups of adjacent pixels,” whether pixels are adjacent to one another is based on distance).
Regarding claim 12 Lee discloses all of the limitations of claim 8. Additionally, Lee discloses wherein the at least one processor is configured to compare a shape of the at least one cluster with a preset shape to identify a target cluster corresponding to the target object (see at least [0017]; “The reflectors 110 may be configured in one or more shapes. For example, the reflectors 110 may have a hemispherical shape, with the curved portion forming the reflective surface 111, and the flat surface configured for mounting to the object 102. A reflector 110 having a hemispherical shape will generally reflect a circular pattern of light or other wave (e.g., infrared (IR)) regardless of the angle from which the light or other wave impacts the reflector 110. Other shapes may be used additionally or alternatively. For example, different sizes and/or shapes of reflectors may be used on different objects, with each object having a unique size and/or shape of reflector for conveniently distinguishing between different objects based on reflector size and/or shape. As another example, different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors. It may be noted that in various embodiments, uniformly sized and shaped reflectors may be employed,” and [0040]; “additional information such as size and/or shape of reflector may be cataloged or tabulated for each reflector. A group of cataloged information describing the reflectors attached to a given object may be cataloged for each object to be analyzed).
Regarding claim 15 Lee discloses a computer-readable recording medium having instructions stored therein (see at least [0023]; “The processing unit 130 includes a memory 132 that stores instructions for directing the processing unit 130, for example, to perform tasks, processes, or flowcharts discussed herein ( or aspects thereof).”), wherein the instructions are executed by the computer to cause the computer to:
acquire first point cloud data including distance and reflection intensity information using a 2D LiDAR sensor of a mobile robot (see at least [0022]; “the depicted detector 120 is configured to acquire both range information and intensity information, which may be represented in separate images as shown in fig. 2and 3,” and [0041]; “range information and intensity information is acquired with a detector…the range information is 3-dimensional in nature and corresponds to the distance from the detector of the portions of an environment including the object that are within a field of view of the detector,” the 3-dimensional information of the surrounding environment corresponds to first point cloud data);
extract second point cloud data corresponding to a high-brightness reflective material included in a marker attached to an object from the first point cloud data(see at least [0026]; “after successful initial thresholding is performed, the majority of the intensity image is removed leaving primarily reflectors or targets of interest in the intensity image. Next, the remaining pixels having a value of 1 may be analyzed to separate reflectors from non-target pixels,” the remaining pixels correspond to the second point cloud data);
cluster the second point cloud data into at least one cluster (see at least [0026]; “after a successful initial thresholding, the majority of the intensity image is removed leaving primarily reflectors or targets of interest”);
identify a target cluster corresponding to the target object among the at least one cluster (see at least [0013]; “identifying one or more objects (e.g., distinguishing objects from other objects, determining a position of an object, determining an orientation of an object) using intensity and range information obtained via a detector,” [0017]; “For example, different sizes and/or shapes of reflectors may be used on different objects, with each object having a unique size and/or shape of reflector for conveniently distinguishing between different objects based on reflector size and/or shape. As another example, different reflectors on the same object may have different sizes or shapes for distinguishing between particular reflectors. It may be noted that in various embodiments, uniformly sized and shaped reflectors may be employed” the information received from the detectors is analyzed to differentiate between objects, information corresponding to specific reflectors are grouped together as being associated with a singular object); and
estimate a pose of the target object based on change in the target cluster (see at least [0045]; “At 422, the object is identified using the correlated locations of the reflectors. With the spatial relationship of the array of detectors to the object known a priori, once the location of each reflector is determined, the position and location of the array may be determined, and accordingly the position and location of the object may be determined, as well as the orientation of the object, based on the determined reflector positions and the knowledge of the spatial relationship between the reflectors and the object. Identification of the object in various embodiments includes one or more of distinguishing the object from other objects, identifying a location of the object, or identifying an orientation of the object. For example, at 424, a pose (position and orientation) of the object is determined.”).
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.
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.
Claim(s) 6, 7, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lee as applied to claims 1 and 8 above, in view of US-11327178 (hereinafter, “Li”).
Regarding claim 6 Lee discloses all of the limitations of claim 1. Lee does not disclose wherein the estimating comprises:
dividing the target cluster into a first group and a second group based on position information of the points of the point cloud data included in the target cluster (see at least [0026]; “The pixels may be organized into groups of adjacent pixels. The size of the groups of pixels having a value of 1 may be used to remove false positives. Depending on the resolution of the intensity information, knowledge of the optics of the detector 120, and geometric data of the reflectors 110, a threshold on the number of pixels in each group may be employed. For example, pixel groups having a size too small or too large to correspond to the expected size may be removed. As another example, a shape filter may be employed, and pixel groups that do not correspond to an expected shape may be removed. For instance, for hemispherical reflectors, a circular shape is expected. Those groups of pixels that substantially differ from a circular shape may be removed. In various embodiments, the processing unit 130 may determine a centroid for each detect (e.g., pixel group) that passed the various thresholds and/or filters, and use each centroid as a 2D location for each reflector 110 on the image 200,” the information associated with the object is divided into groups based on each reflector; Lee demonstrates the object as having four reflectors but it may have more or less (see at least [0016])).
Lee does not teach wherein the estimating comprises:
…acquiring a ratio between the number of the points of the point cloud data included in the first group and the number of the points of the point cloud data included in the second group; and
estimating the pose of the target object based on the change in the ratio
Li, in the same field of endeavor, teaches wherein the estimating comprises:
…acquiring a ratio between the number of the points of the point cloud data included in the first group and the number of the points of the point cloud data included in the second group (see at least [Col. 3, lines 46-58]; “the density of point cloud data from LiDAR sensor generally decreases for objects and other features positioned farther away from the sensor. That is, the point cloud is typically not uniform in density and generally includes more points for objects closer to the sensor (e.g., higher density data and fewer points for objects further from the sensor (e.g., lower density data)”); and
estimating the pose of the target object based on the change in the ratio (see at least [Col. 3, lines 46-58]; “the density of point cloud data from LiDAR sensor generally decreases for objects and other features positioned farther away from the sensor. That is, the point cloud is typically not uniform in density and generally includes more points for objects closer to the sensor (e.g., higher density data and fewer points for objects further from the sensor (e.g., lower density data),” it would be obvious to a person of ordinary skill in the art that based on detecting that one portion of the object is a further distance (less dense points) than another portion of the object, the pose can be roughly estimated based on the difference in distance (or density) between the two points of this object, and Fig. 7; a flow chart illustrating an example operation of an apparatus configured to perform pose estimation in accordance with one example of this disclosure; step 106 states “output environment perception data from the piecewise network structure” environment perception data includes pose estimation [Col. 14, lines 48-51]).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the post estimation method of Lee with the point cloud density pose estimation of Li. One of ordinary skill in the art would have been motivated to make this modification for the benefit to “increase the overall accuracy of environment perception tasks, reduce computation cycles, and lower the number of ambiguous results due to lower density data at longer ranges” (see at least Li; [Col. 9, lines 1-18).
Regarding claim 7 Lee discloses all of the limitations of claim 1. Lee does not disclose further comprising: controlling a behavior of the mobile robot based on the estimated pose of the target object.
Li, in the same field of endeavor, teaches further comprising: controlling a behavior of the mobile robot based on the estimated pose of the target object (see at least [Col. 12, lines 43-57]; “autonomous driving application 52 may be configured to receive perception data 32 and make autonomous driving decisions based on the data. Other applications 54 represent various other contexts in which perception data 32 may be used in other contexts. For example, the poses and locations of persons output by LiDAR-based environment perception module 40 may be used in various applications for body language recognition, motion understanding (e.g., traffic, police officers, emergency services personnel, or other personnel signaling/directing traffic), attention and intention detection (e.g., pedestrians waiting/crossing streets), movies, animation, gaming, robotics, human-computer interaction, machine learning, virtual reality, alternative reality, surveillance, abnormal behavior detection, and public security.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the post estimation method of Lee with the autonomous control of Li. One of ordinary skill in the art would have been motivated to make this modification for the benefit to “use the predicted or inferred intention and attention of a person from the estimated pose to determine driving behaviors” (see at least Li; [Col. 9, lines 1-18).
Regarding claim 13 Lee discloses all of the limitations of claim 8. Additionally, Lee discloses wherein the at least one processor is configured to
divide the target cluster into a first group and a second group based on position information of the points of the point cloud data included in the target cluster (see at least [0026]; “The pixels may be organized into groups of adjacent pixels. The size of the groups of pixels having a value of 1 may be used to remove false positives. Depending on the resolution of the intensity information, knowledge of the optics of the detector 120, and geometric data of the reflectors 110, a threshold on the number of pixels in each group may be employed. For example, pixel groups having a size too small or too large to correspond to the expected size may be removed. As another example, a shape filter may be employed, and pixel groups that do not correspond to an expected shape may be removed. For instance, for hemispherical reflectors, a circular shape is expected. Those groups of pixels that substantially differ from a circular shape may be removed. In various embodiments, the processing unit 130 may determine a centroid for each detect (e.g., pixel group) that passed the various thresholds and/or filters, and use each centroid as a 2D location for each reflector 110 on the image 200,” the information associated with the object is divided into groups based on each reflector; Lee demonstrates the object as having four reflectors but it may have more or less (see at least [0016])).
Lee does not teach
… acquire a ratio between the number of the points of the point cloud data included in the first group and the number of the points of the point cloud data included in the second group; and
estimate the pose of the target object based on the change in the ratio.
Li, in the same field of endeavor, teaches
…acquire a ratio between the number of the points of the point cloud data included in the first group and the number of the points of the point cloud data included in the second group (see at least [Col. 3, lines 46-58]; “the density of point cloud data from LiDAR sensor generally decreases for objects and other features positioned farther away from the sensor. That is, the point cloud is typically not uniform in density and generally includes more points for objects closer to the sensor (e.g., higher density data and fewer points for objects further from the sensor (e.g., lower density data)”); and
estimate the pose of the target object based on the change in the ratio (see at least [Col. 3, lines 46-58]; “the density of point cloud data from LiDAR sensor generally decreases for objects and other features positioned farther away from the sensor. That is, the point cloud is typically not uniform in density and generally includes more points for objects closer to the sensor (e.g., higher density data and fewer points for objects further from the sensor (e.g., lower density data),” it would be obvious to a person of ordinary skill in the art that based on detecting that one portion of the object is a further distance (less dense points) than another portion of the object, the pose can be roughly estimated based on the difference in distance (or density) between the two points of this object, and Fig. 7; a flow chart illustrating an example operation of an apparatus configured to perform pose estimation in accordance with one example of this disclosure; step 106 states “output environment perception data from the piecewise network structure” environment perception data includes pose estimation [Col. 14, lines 48-51]).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the post estimation method of Lee with the point cloud density pose estimation of Li. One of ordinary skill in the art would have been motivated to make this modification for the benefit to “increase the overall accuracy of environment perception tasks, reduce computation cycles, and lower the number of ambiguous results due to lower density data at longer ranges” (see at least Li; [Col. 9, lines 1-18).
Regarding claim 14 Lee discloses all of the limitations of claim 8. Additionally, Lee does not disclose wherein the at least one processor is further configured to control a behavior of the mobile robot based on the estimated pose of the target object.
Li, in the same field of endeavor, teaches wherein the at least one processor is further configured to control a behavior of the mobile robot based on the estimated pose of the target object (see at least [Col. 12, lines 43-57]; “autonomous driving application 52 may be configured to receive perception data 32 and make autonomous driving decisions based on the data. Other applications 54 represent various other contexts in which perception data 32 may be used in other contexts. For example, the poses and locations of persons output by LiDAR-based environment perception module 40 may be used in various applications for body language recognition, motion understanding (e.g., traffic, police officers, emergency services personnel, or other personnel signaling/directing traffic), attention and intention detection (e.g., pedestrians waiting/crossing streets), movies, animation, gaming, robotics, human-computer interaction, machine learning, virtual reality, alternative reality, surveillance, abnormal behavior detection, and public security.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the post estimation method of Lee with the autonomous control of Li. One of ordinary skill in the art would have been motivated to make this modification for the benefit to “use the predicted or inferred intention and attention of a person from the estimated pose to determine driving behaviors” (see at least Li; [Col. 9, lines 1-18).
Conclusion
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/ASHLEIGH NICOLE TURNBAUGH/Examiner, Art Unit 3666
/TIFFANY P YOUNG/Primary Examiner, Art Unit 3666