DETAILED ACTION
This action is in reply to an application which is a continuation of application number 17/360,828 filed March 24th, 2026. Claims 1-20 are currently pending.
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 filed March 24th, 2026 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein regarding the foreign patent document and non-patent literature publication has not been considered.
Drawings
The drawings were received on March 24th, 2026. These drawings are acceptable.
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.
Claim 10 is 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.
Claim 10 recites “a second threshold amount of data points per unit distance” which implies a first threshold amount of data points per unit distance however there is no recitation of a first threshold amount of data points per unit distance in claims 1, 8, or 10. As such, there is insufficient antecedent basis for the “second threshold amount of data points per unit distance” in the claim.
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.
Claims 1-7, 11, 12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Feyereisen et al. (US Pub. No. 20100039294 A1), herein after Feyereisen, and further in view of McNeill; William (US Pub. No. 20170283087 A1), herein after McNeill.
Regarding claim 1, Feyereisen teaches [a] navigation system for a vehicle, comprising (Feyereisen: Para. 0016, teaching a system and method for navigating an aerial vehicle in a runway): memory; and one or more processors, coupled to the memory, configured to cause the navigation system to (Feyereisen: Para. 0018, teaching a system that includes processors; and Para. 0020, teaching that the system uses memory to store the instructions that are executed on the processors): illuminate a surface to cause light to be reflected from the surface; detect the light reflected from the surface (Feyereisen: Para. 0025, teaching the use of sensors such as visible, low-light TV, or lidar sensors to detect light reflected of the surface or any object on the surface); identify, within the data, a subset of the data that corresponds to a border… (Feyereisen: Para. 0034, teaching identifying visually distinguishable features from the sensor data; and Para. 0029, teaching that the visually distinguishable features includes features of a runway such as its edges and markings).
Feyereisen is silent to the system being controlled to generate data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identify, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity; and cause navigation of the vehicle based on a position of the border indicated by the subset of the data.
In a similar field, McNeill teaches generate data representing the light, wherein the data maps intensities of the light to respective positions on the surface (McNeill: Para. 0005 and 0016, teaching generating point cloud data from scans made using a LIDAR system which includes data on the intensity of the light reflected off the surface); identify, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity (McNeill: Para. 0019, teaching filtering the LIDAR point cloud data based on the intensity of the light and identifying edges and markings based on the intensity of light; and Para. 0020, teaching that the different intensities of light can denote features of a runway such as paint markings or patches of grass or gravel); and cause navigation of the vehicle based on a position of the border indicated by the subset of the data (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings) for the benefit of determining the location of runway elements in low visibility conditions.
It would have been obvious to one ordinarily skilled in the art before the filling of the application to modify the use of sensors such as LIDAR from Feyereisen to use the intensity of the reflected light as part of the runway element detection and navigation, as taught by McNeill, for the benefit of determining the location of runway elements in low visibility conditions.
Regarding claim 2, Feyereisen and McNeill remain as applied as in claim 1, and Feyereisen goes on to further teach [t]he navigation system of claim 1, wherein: the border is a first border, the subset of the data is a first subset of the data, the one or more processors are further configured to cause the navigation system to identify a second subset of the data that corresponds to a second border (Feyereisen: Para. 0028, teaching using sensor data to identify runway features; and Para. 0029, teaching determining multiple runways and runway features from the sensor data), and McNeil goes on to further teach the navigation of the vehicle is further based on a second position of the second border indicated by the second subset of the data (examiner interprets that the navigation caused based on the first border and second border is navigation involving navigating a runway during taxing, take-off, and landing in relation to the location of runway elements and the runway’s layout) (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings).
Regarding claim 3, Feyereisen and McNeill remain as applied as in claim 2, and Feyereisen goes on to further teach [t]he navigation system of claim 2, wherein the second border is substantially parallel to the first border (Feyereisen: Para. 0029, teaching determining a runway layout defined by two straight and parallel edges).
Regarding claim 4, Feyereisen and McNeill remain as applied as in claim 3, and Feyereisen goes on to further teach [t]he navigation system of claim 3, wherein the one or more processors are further configured to cause the navigation system to: identify a third subset of the data that corresponds to a third border that intersects the first border and the second border (Feyereisen: Para. 0029, teaching determining two runways that are intersecting each other), and McNeil goes on to further teach wherein the navigation of the vehicle is further based on a third position of the third border indicated by the third subset of the data (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings).
Regarding claim 5, Feyereisen and McNeill remain as applied as in claim 2, and Feyereisen goes on to further teach [t]he navigation system of claim 2, wherein the first border intersects the second border (Feyereisen: Para. 0029, teaching determining two runways that are intersecting each other).
Regarding claim 6, Feyereisen and McNeill remain as applied as in claim 5, and Feyereisen goes on to further teach [t]he navigation system of claim 5, wherein the one or more processors are further configured to cause the navigation system to: determine an angle at which the first border intersects the second border and a position of the angle (Feyereisen: Para. 0029, teaching determining the shape and layout of runway features including points where they intersect), and McNeil goes on to further teach wherein the navigation of the vehicle is further based on the position and the angle (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings).
Regarding claim 7, Feyereisen and McNeill remain as applied as in claim 2, and Feyereisen goes on to further teach [t]he navigation system of claim 2, wherein the one or more processors are further configured to cause the navigation system to: determine that the first border forms a particular shape with the second border (examiner interprets that the particular shape formed is a corner of two runways that are intersecting) (Feyereisen: Para. 0029, teaching determining the shape and layout of runway features including points where they intersect), and McNeil goes on to further teach wherein the navigation of the vehicle is further based on determining that the first border forms the particular shape with the second border (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings).
Regarding claim 11, Feyereisen and McNeill remain as applied as in claim 1, and Feyereisen goes on to further teach [t]he navigation system of claim 1, wherein the navigation of the vehicle is further based on data collected by an inertial navigation system of the vehicle (Feyereisen: Para. 0018, teaching that data used for navigation can include data from inertial sensors).
Regarding claim 12, Feyereisen and McNeill remain as applied as in claim 1, and McNeill goes on to further teach [t]he navigation system of claim 1, wherein the average intensity is a first average intensity and the threshold intensity is a first threshold intensity, and wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: determine parameters of a line that corresponds to the border (McNeill: Para. 0019, teaching filtering the LIDAR point cloud data based on the intensity of the light and identifying edges and markings based on the intensity of light); determine that a first region of the surface on a first side of the line has the first average intensity that is greater than the first threshold intensity; and determine that a second region of the surface on a second side of the line has a second average intensity that is less than a second threshold intensity (McNeill: Para. 0020, teaching that the different intensities of light can denote features of a runway such as paint markings or patches of grass or gravel).
Regarding claim 14, Feyereisen and McNeill remain as applied as in claim 1, and Feyereisen goes on to further teach [t]he navigation system of claim 1, wherein the vehicle is a fixed wing aircraft, a helicopter, a rotorcraft, an unmanned aerial vehicle, a drone, a satellite, or a spacecraft (Feyereisen: Para. 0017, teaching that the invention is for an aircraft such as an airplane, helicopter, or the like).
Regarding claim 15, Feyereisen and McNeill remain as applied as in claim 1, and McNeill goes on to further teach [t]he navigation system of claim 1, wherein the subset of the data is a first subset of the data, and wherein the one or more processors are further configured to cause the navigation system to: identify a second subset of the data that does not correspond to a border (McNeill: Para. 0020, teaching that the different intensities of light can denote features of a runway such as it paint markings or patches of grass or gravel and filtering out data that is below an intensity which do not correspond to numbers and markings); and delete the second subset of the data (McNeill: Para. 0019, teaching filtering out data that are below an intensity and only retaining the higher intensity markings).
Regarding claim 16, Feyereisen and McNeill remain as applied as in claim 1, and Feyereisen goes on to further teach [t]he navigation system of claim 1, further comprising: a light source configured to illuminate the surface; and a light sensor configured to detect the light reflected from the surface (Feyereisen: Para. 0025, teaching the use of sensors such as visible, low-light TV, or lidar sensors to detect light reflected of the surface or any object on the surface).
Regarding claim 17, Feyereisen teaches [a] method of operating a navigation system of a vehicle, the method comprising (Feyereisen: Para. 0016, teaching a system and method for navigating an aerial vehicle in a runway): illuminating a surface to cause light to be reflected from the surface; detecting the light reflected from the surface (Feyereisen: Para. 0025, teaching the use of sensors such as visible, low-light TV, or lidar sensors to detect light reflected of the surface or any object on the surface); identifying, within the data, a subset of the data that corresponds to a border… (Feyereisen: Para. 0034, teaching identifying visually distinguishable features from the sensor data; and Para. 0029, teaching that the visually distinguishable features includes features of a runway such as its edges and markings).
Feyereisen is silent to generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identifying, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity; and causing navigation of the vehicle based on a position of the border indicated by the subset of the data.
In a similar field, McNeill teaches generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface (McNeill: Para. 0005 and 0016, teaching generating point cloud data from scans made using a LIDAR system which includes data on the intensity of the light reflected off the surface); identifying, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity (McNeill: Para. 0019, teaching filtering the LIDAR point cloud data based on the intensity of the light and identifying edges and markings based on the intensity of light; and Para. 0020, teaching that the different intensities of light can denote features of a runway such as paint markings or patches of grass or gravel); and causing navigation of the vehicle based on a position of the border indicated by the subset of the data (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings) for the benefit of determining the location of runway elements in low visibility conditions.
It would have been obvious to one ordinarily skilled in the art before the filling of the application to modify the use of sensors such as LIDAR from Feyereisen to use the intensity of the reflected light as part of the runway element detection and navigation, as taught by McNeill, for the benefit of determining the location of runway elements in low visibility conditions.
Regarding claim 18, Feyereisen and McNeill remain as applied as in claim 17, and McNeill goes on to further teach [t]he method of claim 17, wherein the average intensity is a first average intensity and the threshold intensity is a first threshold intensity, and wherein identifying the subset of the data comprises: determining parameters of a line that corresponds to the border (McNeill: Para. 0019, teaching filtering the LIDAR point cloud data based on the intensity of the light and identifying edges and markings based on the intensity of light); determining that a first region of the surface on a first side of the line has the first average intensity that is greater than the first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is less than a second threshold intensity (McNeill: Para. 0020, teaching that the different intensities of light can denote features of a runway such as paint markings or patches of grass or gravel).
Regarding claim 19, Feyereisen teaches [a] non-transitory computer readable medium storing instructions that, when executed by a navigation system of vehicle, cause the navigation system to perform functions comprising (Feyereisen: Para. 0018, teaching a system that includes processors; and Para. 0020, teaching that the system uses memory to store the instructions that are executed on the processors): illuminating a surface to cause light to be reflected from the surface; detecting the light reflected from the surface (Feyereisen: Para. 0025, teaching the use of sensors such as visible, low-light TV, or lidar sensors to detect light reflected of the surface or any object on the surface); identifying, within the data, a subset of the data that corresponds to a border… (Feyereisen: Para. 0034, teaching identifying visually distinguishable features from the sensor data; and Para. 0029, teaching that the visually distinguishable features includes features of a runway such as its edges and markings).
Feyereisen is silent to generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identifying, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity; and cause navigation of the vehicle based on a position of the border indicated by the subset of the data.
In a similar field, McNeill teaches generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface (McNeill: Para. 0005 and 0016, teaching generating point cloud data from scans made using a LIDAR system which includes data on the intensity of the light reflected off the surface); identifying, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity (McNeill: Para. 0019, teaching filtering the LIDAR point cloud data based on the intensity of the light and identifying edges and markings based on the intensity of light; and Para. 0020, teaching that the different intensities of light can denote features of a runway such as paint markings or patches of grass or gravel); and causing navigation of the vehicle based on a position of the border indicated by the subset of the data (McNeill: Para. 0022, teaching autonomous control of a UAV during landing based on detected runway markings) for the benefit of determining the location of runway elements in low visibility conditions.
It would have been obvious to one ordinarily skilled in the art before the filling of the application to modify the use of sensors such as LIDAR from Feyereisen to use the intensity of the reflected light as part of the runway element detection and navigation, as taught by McNeill, for the benefit of determining the location of runway elements in low visibility conditions.
Regarding claim 20, Feyereisen and McNeill remain as applied as in claim 19, and McNeill goes on to further teach [t]he non-transitory computer readable medium of claim 19, wherein the average intensity is a first average intensity and the threshold intensity is a first threshold intensity, and wherein identifying the subset of the data comprises: determining parameters of a line that corresponds to the border (McNeill: Para. 0019, teaching filtering the LIDAR point cloud data based on the intensity of the light and identifying edges and markings based on the intensity of light); determining that a first region of the surface on a first side of the line has the first average intensity that is greater than the first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is less than a second threshold intensity (McNeill: Para. 0020, teaching that the different intensities of light can denote features of a runway such as paint markings or patches of grass or gravel).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Feyereisen in view of McNeill as applied to claim 1 above, and further in view of Scott et al. (US Pub. No. 20220024486 A1), herein after Scott.
Regarding claim 8, Feyereisen and McNeill remain as applied as in claim 1, however they are silent to [t]he navigation system of claim 1, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: select two arbitrary data points of the data; determine parameters of a curve that includes the two arbitrary data points; and determine that each data point of the subset of the data conforms to the curve within a margin of error and that the subset of the data includes at least a threshold amount of data points.
In a similar field, Scott teaches [t]he navigation system of claim 1, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: select two arbitrary data points of the data; determine parameters of a curve that includes the two arbitrary data points; and determine that each data point of the subset of the data conforms to the curve within a margin of error and that the subset of the data includes at least a threshold amount of data points (Scott: Para. 0123, teaching the use of line fitting methods such as random sample consensus (RANSAC) to fit lines to points of data; and Para. 0124, teaching using models such as RANSAC to fit lines to at least two randomly selected points, comparing the distance between the line and the other data points, and then iterating the line fitting with new arbitrary data points till every point in an area is within a threshold distance of the best fit line) for the benefit of efficiently detecting features on the ground utilizing the minimum observation set size for object identification.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the applicant’s claimed invention to modify the identification of objects and features in a runway using light based sensors from Feyereisen in view of McNeill with the line fitting techniques such as RANSAC to identify the features, as taught by Scott, for the benefit of efficiently detecting features on the ground utilizing the minimum observation set size for object identification.
Regarding claim 9, Feyereisen, McNeill, and Scott remain as applied as in claim 8, and Scott goes on to further teach [t]he navigation system of claim 8, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: select a second two arbitrary data points of the data; determine parameters of a second curve that includes the second two arbitrary data points; and determine that less than the threshold amount of data points of the data conform to the second curve within the margin of error (Scott: Para. 0123, teaching the use of line fitting methods such as random sample consensus (RANSAC) to fit lines to points of data; and Para. 0124, teaching using models such as RANSAC to fit lines to at least two randomly selected points, comparing the distance between the line and the other data points, and then iterating the line fitting with new arbitrary data points till every point in an area is within a threshold distance of the best fit line).
Regarding claim 10, Feyereisen, McNeill, and Scott remain as applied as in claim 8, and Scott goes on to further teach [t]he navigation system of claim 8, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: determine that the subset of the data includes at least a second threshold amount of data points per unit distance (Scott: Para. 0126, teaching that the data used in line fitting is discarded if there are not enough data points or if the data points are too distant from each other).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Feyereisen in view of McNeill as applied to claim 1 above, and further in view of Dame et al. (US Pub. No. 20200027362 A1), herein after Dame.
Regarding claim 13, Feyereisen and McNeill remain as applied as in claim 1, however they are silent to [t]he navigation system of claim 1, wherein the vehicle is an aircraft, and wherein, to cause navigation of the vehicle, the one or more processors are configured to cause the navigation system to: adjust a control surface or propel the aircraft via motorized landing gear of the aircraft based on the position of the border indicated by the subset of the data.
In a similar field, Dame teaches [t]he navigation system of claim 1, wherein the vehicle is an aircraft, and wherein, to cause navigation of the vehicle, the one or more processors are configured to cause the navigation system to: adjust a control surface or propel the aircraft via motorized landing gear of the aircraft based on the position of the border indicated by the subset of the data (Dame: Para. 0030, teaching controlling an aircraft to travel on the surface autonomously based on sensor data detecting airport markings) for the benefit of reducing the need for pilot input during navigation of the aircraft.
It would have been obvious to one ordinarily skilled in the art before the filling of the application to modify the autonomous navigation based on sensed boundaries of an airport’s runway from Feyereisen in view of McNeill to utilize border detection of the runway during autonomous taxiing on the surface, as taught by Dame, for the benefit of reducing the need for pilot input during navigation of the aircraft.
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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4, 6-8, 11, and 13-19 of U.S. Patent No. 12307910 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because independent claims 1, 17, and 19 of the instant application are broader in scope than the independent claim 1, 18, and 19 of the patent application but have the limitation of “identifying the subset of the data further comprises determining that the subset of the data has an average intensity that is greater than a threshold intensity” from dependent claim 13 of the patent. As such, one ordinarily skilled in the art would recognize that the combination of independent claims 1, 18, and 19 of the patent in view of dependent claim 13 of the patent would render obvious the claimed invention and would be motivated to do so for the benefit of reducing erroneous or irrelevant data points.
The following chart breaks down which claim from the patent teaches which limitation from the instant application:
Instant Application
Patent
Claim 1. A navigation system for a vehicle, comprising: memory; and one or more processors, coupled to the memory, configured to cause the navigation system to: illuminate a surface to cause light to be reflected from the surface; detect the light reflected from the surface; generate data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identify, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity; and cause navigation of the vehicle based on a position of the border indicated by the subset of the data.
Claim 1. A navigation system for an aircraft comprising: a light source; a light sensor; one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the navigation system to perform functions comprising: illuminating a surface using the light source to cause light to be reflected from the surface; detecting the light and generating data representing the light using the light sensor, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 13. The navigation system of claim 1, wherein identifying the subset of the data further comprises determining that the subset of the data has an average intensity that is greater than a threshold intensity.
Claim 2. The navigation system of claim 1, wherein: the border is a first border, the subset of the data is a first subset of the data, the one or more processors are further configured to cause the navigation system to identify a second subset of the data that corresponds to a second border, and the navigation of the vehicle is further based on a second position of the second border indicated by the second subset of the data.
Claim 2. The navigation system of claim 1, wherein the border is a first border, the functions further comprising: identifying a second subset of the data that corresponds to a second border, wherein causing navigation of the aircraft comprises causing navigation of the aircraft additionally based on a second position of the second border indicated by the second subset of the data.
Claim 3. The navigation system of claim 2, wherein the second border is substantially parallel to the first border.
Claim 3. The navigation system of claim 2, wherein the second border is substantially parallel to the first border.
Claim 4. The navigation system of claim 3, wherein the one or more processors are further configured to cause the navigation system to: identify a third subset of the data that corresponds to a third border that intersects the first border and the second border, wherein the navigation of the vehicle is further based on a third position of the third border indicated by the third subset of the data.
Claim 4. The navigation system of claim 3, the functions further comprising: identifying a third subset of the data that corresponds to a third border that intersects the first border and the second border, wherein causing navigation of the aircraft comprises causing navigation of the aircraft additionally based on a third position of the third border indicated by the third subset of the data.
Claim 5. The navigation system of claim 2, wherein the first border intersects the second border.
Claim 6. The navigation system of claim 2, wherein the first border intersects the second border.
Claim 6. The navigation system of claim 5, wherein the one or more processors are further configured to cause the navigation system to: determine an angle at which the first border intersects the second border and a position of the angle, wherein the navigation of the vehicle is further based on the position and the angle.
Claim 7. The navigation system of claim 6, the functions further comprising: determining an angle at which the first border intersects the second border and a position of the angle, wherein causing navigation of the aircraft comprises causing navigation of the aircraft additionally based on the position and the angle.
Claim 7. The navigation system of claim 2, wherein the one or more processors are further configured to cause the navigation system to: determine that the first border forms a particular shape with the second border, wherein the navigation of the vehicle is further based on determining that the first border forms the particular shape with the second border.
Claim 8. The navigation system of claim 2, the functions further comprising: determining that the first border forms a particular shape with the second border, wherein causing navigation of the aircraft comprises causing navigation of the aircraft additionally based on determining that the first border forms the particular shape with the second border.
Claim 8. The navigation system of claim 1, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: select two arbitrary data points of the data; determine parameters of a curve that includes the two arbitrary data points; and determine that each data point of the subset of the data conforms to the curve within a margin of error and that the subset of the data includes at least a threshold amount of data points.
Claim 1. A navigation system for an aircraft comprising: a light source; a light sensor; one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the navigation system to perform functions comprising: illuminating a surface using the light source to cause light to be reflected from the surface; detecting the light and generating data representing the light using the light sensor, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 9. The navigation system of claim 8, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: select a second two arbitrary data points of the data; determine parameters of a second curve that includes the second two arbitrary data points; and determine that less than the threshold amount of data points of the data conform to the second curve within the margin of error.
Claim 11. The navigation system of claim 1, wherein identifying the subset of the data further comprises: selecting a second two arbitrary data points of the data; determining parameters of a second curve that includes the second two arbitrary data points; and determining that less than the threshold amount of data points of the data conform to the second curve within the margin of error.
Claim 10. The navigation system of claim 8, wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: determine that the subset of the data includes at least a second threshold amount of data points per unit distance.
Claim 1. A navigation system for an aircraft comprising: a light source; a light sensor; one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the navigation system to perform functions comprising: illuminating a surface using the light source to cause light to be reflected from the surface; detecting the light and generating data representing the light using the light sensor, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 11. The navigation system of claim 1, wherein the navigation of the vehicle is further based on data collected by an inertial navigation system of the vehicle.
Claim 14. The navigation system of claim 1, wherein causing navigation of the aircraft comprises causing navigation of the aircraft additionally based on data collected by an inertial navigation system of the aircraft.
Claim 12. The navigation system of claim 1, wherein the average intensity is a first average intensity and the threshold intensity is a first threshold intensity, and wherein, to identify the subset of the data, the one or more processors are configured to cause the navigation system to: determine parameters of a line that corresponds to the border; determine that a first region of the surface on a first side of the line has the first average intensity that is greater than the first threshold intensity; and determine that a second region of the surface on a second side of the line has a second average intensity that is less than a second threshold intensity.
Claim 15. The navigation system of claim 1, wherein identifying the subset of the data further comprises: determining parameters of a line that corresponds to the border; determining that a first region of the surface on a first side of the line has a first average intensity that is less than a first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is greater than a second threshold intensity.
Claim 13. The navigation system of claim 1, wherein the vehicle is an aircraft, and wherein, to cause navigation of the vehicle, the one or more processors are configured to cause the navigation system to: adjust a control surface or propel the aircraft via motorized landing gear of the aircraft based on the position of the border indicated by the subset of the data.
Claim 16. The navigation system of claim 1, wherein causing navigation of the aircraft comprises adjusting a control surface or propelling the aircraft via motorized landing gear of the aircraft based on the position of the border indicated by the subset of the data.
Claim 14. The navigation system of claim 1, wherein the vehicle is a fixed wing aircraft, a helicopter, a rotorcraft, an unmanned aerial vehicle, a drone, a satellite, or a spacecraft.
Claim 1. A navigation system for an aircraft comprising: a light source; a light sensor; one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the navigation system to perform functions comprising: illuminating a surface using the light source to cause light to be reflected from the surface; detecting the light and generating data representing the light using the light sensor, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 15. The navigation system of claim 1, wherein the subset of the data is a first subset of the data, and wherein the one or more processors are further configured to cause the navigation system to: identify a second subset of the data that does not correspond to a border; and delete the second subset of the data.
Claim 17. The navigation system of claim 1, wherein the subset is a first subset, the functions further comprising: identifying a second subset of the data that does not correspond to a border; and deleting the second subset of the data.
Claim 16. The navigation system of claim 1, further comprising: a light source configured to illuminate the surface; and a light sensor configured to detect the light reflected from the surface.
Claim 1. A navigation system for an aircraft comprising: a light source; a light sensor; one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the navigation system to perform functions comprising: illuminating a surface using the light source to cause light to be reflected from the surface; detecting the light and generating data representing the light using the light sensor, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 17. A method of operating a navigation system of a vehicle, the method comprising: illuminating a surface to cause light to be reflected from the surface; detecting the light reflected from the surface; generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identifying, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity; and causing navigation of the vehicle based on a position of the border indicated by the subset of the data.
Claim 19. A method of operating a navigation system of an aircraft, the method comprising: illuminating a surface to cause light to be reflected from the surface; detecting the light and generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 13. The navigation system of claim 1, wherein identifying the subset of the data further comprises determining that the subset of the data has an average intensity that is greater than a threshold intensity.
Claim 18. The method of claim 17, wherein the average intensity is a first average intensity and the threshold intensity is a first threshold intensity, and wherein identifying the subset of the data comprises: determining parameters of a line that corresponds to the border; determining that a first region of the surface on a first side of the line has the first average intensity that is greater than the first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is less than a second threshold intensity.
Claim 15. The navigation system of claim 1, wherein identifying the subset of the data further comprises: determining parameters of a line that corresponds to the border; determining that a first region of the surface on a first side of the line has a first average intensity that is less than a first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is greater than a second threshold intensity.
Claim 19. A non-transitory computer readable medium storing instructions that, when executed by a navigation system of vehicle, cause the navigation system to perform functions comprising: illuminating a surface to cause light to be reflected from the surface; detecting the light reflected from the surface; generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identifying, within the data, a subset of the data that corresponds to a border based on the subset of the data having an average intensity that is greater than a threshold intensity; and causing navigation of the vehicle based on a position of the border indicated by the subset of the data.
Claim 18. A non-transitory computer readable medium storing instructions that, when executed by a navigation system of an aircraft, cause the navigation system to perform functions comprising: illuminating a surface to cause light to be reflected from the surface; detecting the light and generating data representing the light, wherein the data maps intensities of the light to respective positions on the surface; identifying within the data a subset of the data that corresponds to a border by: selecting two arbitrary data points of the data; determining parameters of a curve that includes the two arbitrary data points; determining, with respect to the curve, residuals of data points of the subset of the data that are not the two arbitrary data points; and determining that the residuals are each less than a margin of error and that the subset of the data includes at least a threshold amount of data points; and determining that the subset of the data includes at least a second threshold amount of data points per unit distance; and causing navigation of the aircraft on the surface based on a position of the border indicated by the subset of the data.
Claim 13. The navigation system of claim 1, wherein identifying the subset of the data further comprises determining that the subset of the data has an average intensity that is greater than a threshold intensity.
Claim 20. The non-transitory computer readable medium of claim 19, wherein the average intensity is a first average intensity and the threshold intensity is a first threshold intensity, and wherein identifying the subset of the data comprises: determining parameters of a line that corresponds to the border; determining that a first region of the surface on a first side of the line has the first average intensity that is greater than the first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is less than a second threshold intensity.
Claim 15. The navigation system of claim 1, wherein identifying the subset of the data further comprises: determining parameters of a line that corresponds to the border; determining that a first region of the surface on a first side of the line has a first average intensity that is less than a first threshold intensity; and determining that a second region of the surface on a second side of the line has a second average intensity that is greater than a second threshold intensity.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. (US Pub. No. 20190025833 A1) teaches autonomous control of a vehicle based on identification of objects and road markings from a LIDAR sensor.
Funaya; Hiroyuki (US Pub. No. 20200372670 A1) teaches autonomous control of a vehicle using LIDAR sensors to generate data which are analyzed for curve fitting using techniques such as RANSAC.
Wheeler et al. (US Pub. No. 20180188059 A1) teaches autonomous control of a vehicle by monitoring the surroundings of the vehicle using sensors such as LIDAR and applying curve fitting techniques to determine the objects in the surroundings of the vehicle.
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/A.K.M./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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