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 .
Response to Amendment
Examiner acknowledges the reply filed on 01/02/2026 in which claims 1, 8, 9, 16, and 17 have been amended. Currently, claims 1, 8, 9, 16, and 17 are pending for examination in this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 9, and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 2, 4, 9, 10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Olson (US 2018/0210087 A1) in view of Velas (Velas, Martin, Michal Spanel, Tomas Sleziak, Jiri Habrovec, and Adam Herout. "Indoor and outdoor backpack mapping with calibrated pair of velodyne LiDARs." Sensors 19, no. 18 (2019): 3944.).
Regarding Claim 1, Olson discloses a method for global localization ([0041]: “Methods presented in this disclosure may also be implemented as part of simultaneous localization and mapping (SLAM) systems.”) for a dynamic environment using a 3D Light Detection And Ranging (LiDAR) scanner ([0009]: “The three-dimensional point data may be captured using a LiDAR sensor or another type of sensor.”), comprising:
generating a 2D grid map from 3D point cloud data ([0034]: “To partition the scene, points from the 3D point data are projected onto cells on an X-Y plane (e.g., the ground) of the scene.”) acquired using the 3D LiDAR scanner ([0009]: “The three-dimensional point data may be captured using a LiDAR sensor or another type of sensor.”);
searching for a 2D global position of a vehicle on the 2D grid map using data acquired from the 3D LiDAR scanner ([0041], [0043]); and
Olson suggests (Abstract, [0006], [0074]) but does not explicitly teach but Velas does teach mapping the 2D global position to a 6-degrees-of-freedom (6-DOF) position in a 3D space (Page 29, Para 3: “Since our solution integrates precise GNSS/INS module for outdoor scenarios, the model is georeferenced—the coordinates of all the points are bound in some global geodetic frame.”; Figure 15: “The goal is to estimate 6DoF poses P1, P2, . . . , PN of graph nodes (vertices) p1, p2, . . . , p15 in the trajectory… When GNSS subsystem is available (b), additional visual loops are introduced as transformations from the origin O of some local geodetic (orthogonal NED) coordinate frame.”; Figure 7), wherein mapping the 2D global position comprises estimating the 6-DOF position by performing 3D point-cloud matching based on the 2D global position (Page 19: “For outdoor mapping, the absolute position and orientation are provided by the GNSS/INS subsystem with PPK (Post Processed Kinematics) corrections. While the global error of these poses is small, relative frame-to-frame error is much larger when compared to the accuracy of pure SLAM solution. Therefore, we combine our SLAM (in the same way as described above) with additional edges in the pose graph representing the global position in some geodetic frame, as shown in Figure 15b.” Thus the frames (which contain the point could data) are mapped to the 2D position in the GNSS data, as are the 6DOF pose data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olson with the teaching of Velas to map the 6DOF pose, global location, and 3D point clouds together. Velas notes in the abstract that “By fusion with GNSS/INS sub-system, the mapping of featureless environments and the georeferencing of resulting point cloud is possible.” This allows for a broader area of use for the method in question, thus increasing its versatility and overall utility for the end user.
Regarding Claim 2, which depends from rejected Claim 1, Olson further discloses wherein generating the 2D grid map ([0042]: “The three-dimensional point data is then transformed at 42 to a two-dimensional map in the manner described above.”) comprises:
partitioning a 3D space, in which the 3D point cloud data is distributed, into multiple 3D unit spaces, the 3D space being defined with an X-axis, a Y-axis, and a Z- axis ([0035]: “For each cell in the XY grid, the verticality of any detected object is represented at 15 by a column vector (i.e., m×1 matrix), where each element in the column vector corresponds to a different range of heights above some reference height.”) ;
calculating an occupancy probability depending on whether a point is present in multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to each of multiple grid cells acquired by partitioning an XY plane ([0037]: “In this embodiment, the identification of a vertical structure can be performed with a POPCOUNT operation on the column vector: if the number of bits set to 1 exceeds a threshold, the cell passes a verticality test and is marked as a structure.); and
generating the 2D grid map using the occupancy probability of each of the multiple grid cells on the XY plane ([0034]: “To partition the scene, points from the 3D point data are projected onto cells on an X-Y plane (e.g., the ground) of the scene.”).
Regarding Claim 4, which depends from rejected Claim 2, Olson further discloses wherein calculating the occupancy probability is configured to calculate the occupancy probability depending on whether a point is present in multiple 3D unit spaces within a first range of the Z-axis ([0036]: “This range can be tailored to focus on a particular region of interest (i.e. 20 cm to 200 cm) relative to the minimum z-height. This z-height can be dynamically updated as observations are added, for example by shifting the region of interest to be relative to the lowest observed point.”).
Regarding Claim 9, Olson discloses an apparatus for global localization for a dynamic environment using a 3D Light Detection and Ranging (LiDAR) scanner, comprising:
memory in which at least one program is recorded ([0079]); and
a processor for executing the program ([0079], [0080]), wherein the program performs generating a 2D grid map from 3D point cloud data acquired using the 3D LiDAR scanner ([0034]: “To partition the scene, points from the 3D point data are projected onto cells on an X-Y plane (e.g., the ground) of the scene.”) acquired using the 3D LiDAR scanner ([0009]: “The three-dimensional point data may be captured using a LiDAR sensor or another type of sensor.”);
searching for a 2D global position of a vehicle on the 2D grid map using data acquired from the 3D LiDAR scanner ([0041], [0043]); and
Olson suggests (Abstract, [0006], [0074]) but does not explicitly teach but Velas does teach mapping the 2D global position comprises estimating the 6-degrees-of-freedom (6-DOF) position in a 3D space (Page 29, Para 3: “Since our solution integrates precise GNSS/INS module for outdoor scenarios, the model is georeferenced—the coordinates of all the points are bound in some global geodetic frame.”; Figure 15: “The goal is to estimate 6DoF poses P1, P2, . . . , PN of graph nodes (vertices) p1, p2, . . . , p15 in the trajectory… When GNSS subsystem is available (b), additional visual loops are introduced as transformations from the origin O of some local geodetic (orthogonal NED) coordinate frame.”; Figure 7), wherein mapping the 2D global position comprises estimating the 6-DOF position by performing 3D point-cloud matching based on the 2D global position (Page 19: “For outdoor mapping, the absolute position and orientation are provided by the GNSS/INS subsystem with PPK (Post Processed Kinematics) corrections. While the global error of these poses is small, relative frame-to-frame error is much larger when compared to the accuracy of pure SLAM solution. Therefore, we combine our SLAM (in the same way as described above) with additional edges in the pose graph representing the global position in some geodetic frame, as shown in Figure 15b.” Thus the frames (which contain the point could data) are mapped to the 2D position in the GNSS data, as are the 6DOF pose data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Olson with the teaching of Velas to map the 6DOF pose, global location, and 3D point clouds together. Velas notes in the abstract that “By fusion with GNSS/INS sub-system, the mapping of featureless environments and the georeferencing of resulting point cloud is possible.” This allows for a broader area of use for the device in question, thus increasing its versatility and overall utility for the end user.
Regarding Claim 10, which depends from rejected Claim 9, Olson further discloses wherein generating the 2D grid map ([0042]: “The three-dimensional point data is then transformed at 42 to a two-dimensional map in the manner described above.”) comprises:
partitioning a 3D space, in which the 3D point cloud data is distributed, into multiple 3D unit spaces, the 3D space being defined with an X-axis, a Y-axis, and a Z- axis ([0035]: “For each cell in the XY grid, the verticality of any detected object is represented at 15 by a column vector (i.e., m×1 matrix), where each element in the column vector corresponds to a different range of heights above some reference height.”);
calculating an occupancy probability depending on whether a point is present in multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to each of multiple grid cells acquired by partitioning an XY plane ([0037]: “In this embodiment, the identification of a vertical structure can be performed with a POPCOUNT operation on the column vector: if the number of bits set to 1 exceeds a threshold, the cell passes a verticality test and is marked as a structure.); and
generating the 2D grid map using the occupancy probability of each of the multiple grid cells on the XY plane ([0034]: “To partition the scene, points from the 3D point data are projected onto cells on an X-Y plane (e.g., the ground) of the scene.”).
Regarding Claim 12, which depends from rejected Claim 9, Olson further discloses wherein calculating the occupancy probability is configured to calculate the occupancy probability depending on whether a point is present in multiple 3D unit spaces within a first range of the Z-axis ([0036]: “This range can be tailored to focus on a particular region of interest (i.e. 20 cm to 200 cm) relative to the minimum z-height. This z-height can be dynamically updated as observations are added, for example by shifting the region of interest to be relative to the lowest observed point.”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas as applied to Claim 2 above, and further in view of Slutsky (US 2020/0309530 A1).
Regarding Claim 3, which depends from rejected Claim 2, Olson in view of Velas does not teach and Slutsky does teach wherein calculating the occupancy probability is configured to:
set the occupancy probability to '1.0' when a point is present in at least one of the multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to the grid cell ([0047]: “If both radar sensors 32 confirm that an object 316 appears to be located in the space associated with columns 4-5 and rows 6-7 in FIG. 4A and those confirmations are conclusive, for instance, then the method would assign a high probability to the corresponding cells 302 in FIG. 4B (e.g., each of those cells may be assigned a probability of 1.0,”), and
set the occupancy probability to '0.5' by determining the multiple 3D unit spaces to be an unknown area when none of the multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to the grid cell contain points ([0047]: “without any radar sensor data yet, the method may assume there is a 50% chance that the corresponding area 312 is occupied with an object, hence, a probability of 0.5 could be assigned”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olson in view of Velas with the teaching of Slutsky to use 1.0 to represent XY boxes corresponding to columns in which points are known to have occurred and 0.5 to represent XY boxes corresponding to areas with unknown occupancy. Such designations are beneficial to the subsequent operation of lidar-based devices operating in the area, as they provide a simple means to determine passable and impassable regions. Thus, a worker skilled in the art would find the adoption of this method to have predictable results.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas as applied to Claim 9 above, and further in view of Slutsky (US 2020/0309530 A1).
Regarding Claim 11, which depends from rejected Claim 9, Olson in view of Velas does not teach and Slutsky does teach wherein calculating the occupancy probability is configured to:
set the occupancy probability to '1.0' when a point is present in at least one of the multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to the grid cell ([0047]: “If both radar sensors 32 confirm that an object 316 appears to be located in the space associated with columns 4-5 and rows 6-7 in FIG. 4A and those confirmations are conclusive, for instance, then the method would assign a high probability to the corresponding cells 302 in FIG. 4B (e.g., each of those cells may be assigned a probability of 1.0,”), and
set the occupancy probability to '0.5' by determining the multiple 3D unit spaces to be an unknown area when none of the multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to the grid cell contain points ([0047]: “without any radar sensor data yet, the method may assume there is a 50% chance that the corresponding area 312 is occupied with an object, hence, a probability of 0.5 could be assigned”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Olson in view of Velas with the teaching of Slutsky to use 1.0 to represent XY boxes corresponding to columns in which points are known to have occurred and 0.5 to represent XY boxes corresponding to areas with unknown occupancy. Such designations are beneficial to the subsequent operation of lidar-based devices operating in the area, as they provide a simple means to determine passable and impassable regions. Thus, a worker skilled in the art would find the adoption of this teaching to have predictable results.
Claims 5-7 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas as applied to Claim 1 above, and further in view of Schroeter (US 2020/0386555 A1).
Regarding Claim 5, which depends from Claim 1, Olson in view of Velas does not teach and Schroeter does teach wherein searching for the 2D global position ([0065]: “The localization API 250 may be configured to return an accurate location of the corresponding vehicle 150 as latitude and longitude coordinates.”) is configured to assign particle samples to the 2D grid map ([0119]: “the performing of the search within the search space, at action 1635, may include determining a set of particles using a particle-filter based localization method”) and search for a sample that best matches 3D point cloud data acquired from the 3D LiDAR scanner as a current position of the vehicle ([0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olson in view of Velas with the teaching of Schroeter to search within the 2D map of Olson in view of Velas using a particle sampling method to match the 3D LiDAR scanner. Schroeter notes in [0137] that the “particle filter may serve as an independent localization method (distinct from an ICP based method) that can be made lightweight enough to operate constantly during vehicle motion.” This allows for more rapid updates of pose, yielding more accurate and up-to-date results.
Regarding Claim 6, which depends from rejected Claim 5, Olson in view of Velas does not teach and Schroeter further teaches wherein searching for the 2D global position is configured to set areas to which samples are capable of being assigned on the 2D grid map and to assign the samples only to the set areas ([0121]: “The method 1600 may be employed by the vehicle computing system 120a of the vehicle 150a to reduce the search space of the HD map when determining the geographic location of the vehicle 150a, i.e., by limiting the search to places where a vehicle is expected to drive, for example, within navigable boundaries of the nearest road.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Schroeter to narrow the search space to the method of Olson in view of Velas. Schroeter notes in [0121] that this procedure limits the search to the places where the vehicle is most likely to be present, which has obvious benefits in more rapid position retrievals and lower power usage.
Regarding Claim 7, which depends from rejected Claim 5, Olson further discloses wherein searching for the 2D global position is configured to search for a sample that best matches 3D point cloud data within a second range of a Z-axis as the current position of the vehicle ([0036]: “This z-height can be dynamically updated as observations are added, for example by shifting the region of interest to be relative to the lowest observed point.” Thus updating the z-height results in a second range).
Claims 13-15 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas as applied to Claim 9 above, and further in view of Schroeter (US 2020/0386555 A1).
Regarding Claim 13, which depends from rejected Claim 9, Olson in view of Velas does not teach and Schroeter does teach wherein searching for the 2D global position ([0065]: “The localization API 250 may be configured to return an accurate location of the corresponding vehicle 150 as latitude and longitude coordinates.”) is configured to assign particle samples to the 2D grid map ([0119]: “the performing of the search within the search space, at action 1635, may include determining a set of particles using a particle-filter based localization method”) and search for a sample that best matches 3D point cloud data acquired from the 3D LiDAR scanner as a current position of the vehicle ([0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Olson in view of Velas with the teaching of Schroeter to search within the 2D map of Olson in view of Velas using a particle sampling method to match the 3D LiDAR scanner. Schroeter notes in [0137] that the “particle filter may serve as an independent localization method (distinct from an ICP based method) that can be made lightweight enough to operate constantly during vehicle motion.” This allows for more rapid updates of pose, yielding more accurate and up-to-date results.
Regarding Claim 14, which depends from rejected Claim 13, Olson in view of Velas does not teach and Schroeter further teaches wherein searching for the 2D global position is configured to set areas to which samples are capable of being assigned on the 2D grid map and to assign the samples only to the set areas ([0121]: “The method 1600 may be employed by the vehicle computing system 120a of the vehicle 150a to reduce the search space of the HD map when determining the geographic location of the vehicle 150a, i.e., by limiting the search to places where a vehicle is expected to drive, for example, within navigable boundaries of the nearest road.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Schroeter to narrow the search space to the apparatus of Olson in view of Velas. Schroeter notes in [0121] that this procedure limits the search to the places where the vehicle is most likely to be present, which has obvious benefits in more rapid position retrievals and lower power usage.
Regarding Claim 15, which depends from rejected Claim 13, Olson further discloses wherein searching for the 2D global position is configured to search for a sample that best matches 3D point cloud data within a second range of a Z-axis as the current position of the vehicle ([0036]: “This z-height can be dynamically updated as observations are added, for example by shifting the region of interest to be relative to the lowest observed point.” Thus updating the z-height results in a second range).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas as applied to Claim 1 above, and further in view of Yamaguchi (US 2015/0015702 A1).
Regarding Claim 8, which depends from rejected Claim 1, Olson in view of Velas does not teach and Yamaguchi does teach wherein the second global position is represented using X and Y coordinates and a yaw angle on a 2D plane, and mapping the 2D global position is configured to set an initial 6-DOF position by incorporating the X and Y coordinates and the yaw angle, which correspond to the 2D global position, and by setting a Z coordinate, a pitch angle, and a roll angle to 0 ([0087]: “the embodiments can also be applied to the case where three-degree-of-freedom position (forward/backward position, and left/right position) and attitude angle (yaw) are estimated as in an automated guided vehicle for use in a factory or the like without suspensions or the like. Specifically, in such a vehicle, since the vertical position and the roll and pitch of the attitude angle are fixed parameters, these parameters may be measured in advance or found with reference to the three-dimensional map database 3.”; Thus, the 6-DOF variable is initialized with x, y, and yaw values, and the other values (z, roll, and pitch) are fixed parameters which may be set beforehand. It is well known to initialize variable in software with the value of zero before they are populated, and reasonable to assume that in the case where the other parameters are subsequently found based on a map database, that they are initially set to zero.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olson in view of Velas with the teaching of Yamaguchi to initialize a 6-DOF position with preexisting x, y, and yaw values while leaving the remaining values zero. Given that the only parameters available on a 2D surface are x, y, and yaw, any method that incorporates such a 3DOF variable into a 6DOF variable will necessarily have incomplete information to begin with. One skilled in the art wishing to create a 6DOF variable from a 3DOF variable would therefore find the initialization described here to have predictable results.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas as applied to Claim 9 above, and further in view of Yamaguchi (US 2015/0015702 A1).
Regarding Claim 16, which depends from rejected Claim 9, Olson in view of Velas does not teach and Yamaguchi does teach wherein the second global position is represented using X and Y coordinates and a yaw angle on a 2D plane, and mapping the 2D global position is configured to set an initial 6-DOF position by incorporating the X and Y coordinates and the yaw angle, which correspond to the 2D global position, and by setting a Z coordinate, a pitch angle, and a roll angle to 0 ([0087]: “the embodiments can also be applied to the case where three-degree-of-freedom position (forward/backward position, and left/right position) and attitude angle (yaw) are estimated as in an automated guided vehicle for use in a factory or the like without suspensions or the like. Specifically, in such a vehicle, since the vertical position and the roll and pitch of the attitude angle are fixed parameters, these parameters may be measured in advance or found with reference to the three-dimensional map database 3.”; Thus, the 6-DOF variable is initialized with x, y, and yaw values, and the other values (z, roll, and pitch) are fixed parameters which may be set beforehand. It is well known to initialize variable in software with the value of zero before they are populated, and reasonable to assume that in the case where the other parameters are subsequently found based on a map database, that they are initially set to zero.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apparatus the method of Olson in view of Velas with the teaching of Yamaguchi to initialize a 6-DOF position with preexisting x, y, and yaw values while leaving the remaining values zero. Given that the only parameters available on a 2D surface are x, y, and yaw, any method that incorporates such a 3DOF variable into a 6DOF variable will necessarily have incomplete information to begin with. One skilled in the art wishing to create a 6DOF variable from a 3DOF variable would therefore find the initialization described here to have predictable results.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas and further in view of Yamaguchi (US 2015/0015702 A1).
Regarding Claim 17, Olson discloses a method for global localization ([0041]: “Methods presented in this disclosure may also be implemented as part of simultaneous localization and mapping (SLAM) systems.”) for a dynamic environment using a 3D Light Detection And Ranging (LiDAR) scanner ([0009]: “The three-dimensional point data may be captured using a LiDAR sensor or another type of sensor.”), comprising:
partitioning a 3D space in which 3D point cloud data acquired using the 3D LiDAR scanner is distributed into multiple 3D unit spaces, the 3D space being defined with an X-axis, a Y-axis and a Z-axis ([0035]: “For each cell in the XY grid, the verticality of any detected object is represented at 15 by a column vector (i.e., m×1 matrix), where each element in the column vector corresponds to a different range of heights above some reference height.”) ;
calculating an occupancy probability depending on whether a point is present in multiple 3D unit spaces that are aligned in a line along the Z-axis so as to correspond to each of multiple grid cells acquired by partitioning an XY plane ([0037]: “In this embodiment, the identification of a vertical structure can be performed with a POPCOUNT operation on the column vector: if the number of bits set to 1 exceeds a threshold, the cell passes a verticality test and is marked as a structure.); and
generating the 2D grid map using the occupancy probability of each of the multiple grid cells on the XY plane ([0034]: “To partition the scene, points from the 3D point data are projected onto cells on an X-Y plane (e.g., the ground) of the scene.”)
generating a 2D grid map from 3D point cloud data ([0034]: “To partition the scene, points from the 3D point data are projected onto cells on an X-Y plane (e.g., the ground) of the scene.”) acquired using the 3D LiDAR scanner ([0009]: “The three-dimensional point data may be captured using a LiDAR sensor or another type of sensor.”);
searching for a 2D global position of a vehicle on the 2D grid map using data acquired from the 3D LiDAR scanner ([0041], [0043]); and
Olson suggests (Abstract, [0006], [0074]) but does not explicitly teach but Velas does teach mapping the 2D global position to a 6-degrees-of-freedom (6-DOF) position in a 3D space (Page 29, Para 3: “Since our solution integrates precise GNSS/INS module for outdoor scenarios, the model is georeferenced—the coordinates of all the points are bound in some global geodetic frame.”; Figure 15: “The goal is to estimate 6DoF poses P1, P2, . . . , PN of graph nodes (vertices) p1, p2, . . . , p15 in the trajectory… When GNSS subsystem is available (b), additional visual loops are introduced as transformations from the origin O of some local geodetic (orthogonal NED) coordinate frame.”; Figure 7), wherein mapping the 2D global position comprises estimating the 6-DOF position by performing 3D point-cloud matching based on the 2D global position (Page 19: “For outdoor mapping, the absolute position and orientation are provided by the GNSS/INS subsystem with PPK (Post Processed Kinematics) corrections. While the global error of these poses is small, relative frame-to-frame error is much larger when compared to the accuracy of pure SLAM solution. Therefore, we combine our SLAM (in the same way as described above) with additional edges in the pose graph representing the global position in some geodetic frame, as shown in Figure 15b.” Thus the frames (which contain the point could data) are mapped to the 2D position in the GNSS data, as are the 6DOF pose data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Olson with the teaching of Velas to map the 6DOF pose, global location, and 3D point clouds together. Velas notes in the abstract that “By fusion with GNSS/INS sub-system, the mapping of featureless environments and the georeferencing of resulting point cloud is possible.” This allows for a broader area of use for the device in question, thus increasing its versatility and overall utility for the end user.
Olson in view of Velas does not teach and Yamaguchi does teach wherein the second global position is represented using X and Y coordinates and a yaw angle on a 2D plane, and mapping the 2D global position is configured to set an initial 6-DOF position by incorporating the X and Y coordinates and the yaw angle, which correspond to the 2D global position, and by setting a Z coordinate, a pitch angle, and a roll angle to 0 ([0087]: “the embodiments can also be applied to the case where three-degree-of-freedom position (forward/backward position, and left/right position) and attitude angle (yaw) are estimated as in an automated guided vehicle for use in a factory or the like without suspensions or the like. Specifically, in such a vehicle, since the vertical position and the roll and pitch of the attitude angle are fixed parameters, these parameters may be measured in advance or found with reference to the three-dimensional map database 3.”; Thus, the 6-DOF variable is initialized with x, y, and yaw values, and the other values (z, roll, and pitch) are fixed parameters which may be set beforehand. It is well known to initialize variable in software with the value of zero before they are populated, and reasonable to assume that in the case where the other parameters are subsequently found based on a map database, that they are initially set to zero.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apparatus the method of Olson in view of Velas with the teaching of Yamaguchi to initialize a 6-DOF position with preexisting x, y, and yaw values while leaving the remaining values zero. Given that the only parameters available on a 2D surface are x, y, and yaw, any method that incorporates such a 3DOF variable into a 6DOF variable will necessarily have incomplete information to begin with. One skilled in the art wishing to create a 6DOF variable from a 3DOF variable would therefore find the initialization described here to have predictable results.
Regarding Claim 18, which depends from rejected Claim 17, Olson further discloses wherein calculating the occupancy probability is configured to calculate the occupancy probability depending on whether a point is present in multiple 3D unit spaces within a first range of the Z-axis ([0036]: “This range can be tailored to focus on a particular region of interest (i.e. 20 cm to 200 cm) relative to the minimum z-height. This z-height can be dynamically updated as observations are added, for example by shifting the region of interest to be relative to the lowest observed point.”).
Claims 19 and 20 is rejected under 35 U.S.C. 103 as being unpatentable over Olson in view of Velas and further in view of Yamaguchi as applied to Claim 18, and further in view of Schroeter.
Regarding Claim 19, which depends from rejected Claim 18, Olson in view of Velas and further in view of Yamaguchi does not teach and Schroeter does teach wherein searching for the 2D global position ([0065]: “The localization API 250 may be configured to return an accurate location of the corresponding vehicle 150 as latitude and longitude coordinates.”) is configured to assign particle samples to the 2D grid map ([0119]: “the performing of the search within the search space, at action 1635, may include determining a set of particles using a particle-filter based localization method”) and search for a sample that best matches 3D point cloud data acquired from the 3D LiDAR scanner as a current position of the vehicle ([0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olson in view of Velas with the teaching of Schroeter to search within the 2D map of Olson in view of Velas using a particle sampling method to match the 3D LiDAR scanner. Schroeter notes in [0137] that the “particle filter may serve as an independent localization method (distinct from an ICP based method) that can be made lightweight enough to operate constantly during vehicle motion.” This allows for more rapid updates of pose, yielding more accurate and up-to-date results.
Olson in view of Velas does not teach and Schroeter further teaches wherein searching for the 2D global position is configured to set areas to which samples are capable of being assigned on the 2D grid map and to assign the samples only to the set areas ([0121]: “The method 1600 may be employed by the vehicle computing system 120a of the vehicle 150a to reduce the search space of the HD map when determining the geographic location of the vehicle 150a, i.e., by limiting the search to places where a vehicle is expected to drive, for example, within navigable boundaries of the nearest road.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teaching of Schroeter to narrow the search space to the method of Olson in view of Velas. Schroeter notes in [0121] that this procedure limits the search to the places where the vehicle is most likely to be present, which has obvious benefits in more rapid position retrievals and lower power usage.
Regarding Claim 20, which depends from rejected Claim 19, Olson further discloses wherein searching for the 2D global position is configured to search for a sample that best matches 3D point cloud data within a second range of a Z-axis as the current position of the vehicle ([0036]: “This z-height can be dynamically updated as observations are added, for example by shifting the region of interest to be relative to the lowest observed point.” Thus updating the z-height results in a second range).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yamazaki (US 2017/0116487 A1) discloses an apparatus for generating an occupancy grid map constituted with a two-dimensional grid around a first moving body.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN WADE CLOUSER whose telephone number is (571)272-0378. The examiner can normally be reached M-F 7:30 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ISAM ALSOMIRI can be reached at (571) 272-6970. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.W.C./ Examiner, Art Unit 3645
/ISAM A ALSOMIRI/ Supervisory Patent Examiner, Art Unit 3645
Prosecution Insights